Research ArticleFuzzy System of Irrigation Applied to the Growth ofHabanero Pepper (Capsicum chinense Jacq) under ProtectedConditions in Yucatan Mexico
Martha Rocio Ceballos12 Juan Luis Gorricho2 Oscar Palma Gamboa1
Moacutenica Karel Huerta345 David Rivas6 and Mayra Erazo Rodas6
1 Information and Communication Technologies Technological Institute of Conkal 97345 Conkal YUC Mexico2Department of Telematics Engineering Polytechnic University of Catalonia 08034 Barcelona Spain3Department of Electronic Engineering Salesian Polytechnic University EC010105 Cuenca Ecuador4Networks and Applied Telematics Group (GRETA) Simon Bolıvar University Caracas 89000 Venezuela5Prometeo Project Researcher (SENESCYT) Ecuador6Department of Electrical and Electronics Armed Forces University ESPE EC170501 Sangolquı Ecuador
Correspondence should be addressed to Martha Rocio Ceballos marthaceballositconkaledumx
Received 15 November 2014 Revised 8 March 2015 Accepted 20 April 2015
Academic Editor Shaojie Tang
Copyright copy 2015 Martha Rocio Ceballos et al This is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work is properlycited
Agriculture is the largest user of water worldwide by using about 70 percent of total consumption The world food productiondepends on the availability of water considering factors such as demographic and climate change so the use of efficient irrigationis necessary to apply the correct amount of water to cropsThe traditional irrigation systems generally program their scheme basedon measurements made at Class A evaporimeter pan In this paper an irrigation scheme defined by an algorithm that automatesthe amount of water supplied is presented it considers the consumption of habanero pepper crop and a fuzzy system evaluatesthe necessary duration of irrigation The climatic variables considered are temperature relative humidity and soil moisture Thealgorithm was programmed in a microcontroller Atmel 328p included in Arduino platform with the addition of a ZigBee wirelesssystem that allows for monitoring through a PC The climatic variables were inserted into the fuzzy system by sets of trapezoidaland triangular form and aMamdani type inference mechanism in which the knowledge of an expert is registered through the fuzzyrules The system was applied to a habanero pepper crop at Conkal Institute of Technology in Yucatan Mexico
1 Introduction
Mexico ranks second in volume production of chili peppersand third in area harvested with 2379736 tons and 136132 harespectively engaged with the 71 area and 76 ton of worldproduction [1] The fruits of this plant are of great economicimportance because there is a wide variety of applicationsthere is an excellent source of natural dyes minerals andvitamins A C and E the mainly substance that is extractedfrom this crop is capsaicin whose concentration is very highin the variety known as habaneroThere are several regions inMexico who grow habanero pepper [2] however more than
50 of production for domestic and export markets comesfrom the Yucatan Peninsula which has provided support forobtaining distinctive ldquoappellation of originrdquo published in theOfficial Gazette of the Mexican Federation on June 4 2010For production even outside station protected cultivationtechniques are used and the most commonly used structuresin protected agriculture are greenhouses shade mesh highand low tunnels The shadow mesh structure mentionedabove is a simple and inexpensive alternative although verylow technology [3]
Environmental variables have by nature a complex andnonlinear dynamic Therefore the information processing
Hindawi Publishing CorporationInternational Journal of Distributed Sensor NetworksVolume 2015 Article ID 123543 13 pageshttpdxdoiorg1011552015123543
2 International Journal of Distributed Sensor Networks
solutions are required based on advanced techniques andtechnologies to provide a better result in the productionof food [4] In this context the use of soft computingtechniques (such as neural networks genetic algorithmsand fuzzy logic) has been considered as real solutions toproblems related to the effective management of agriculturalresources [5] One such resource is hydric characterizedby irrigation systems and affecting plant growth and henceproduction Due to the above there are proposals that enableautomation and control to carry out this process in thebest way [6 7] These methods adjust the amount of watersupplied according to crop water requirements the amountof water available in the channel and system componentsIn systems that use drip irrigation such information has notbeen considered because the amount of water is controlledin a certain manner [8] However the frequency of irrigationdrip systems still depends on climatic factors soil type andcrop type so that the control and close monitoring remainsnecessary in these systems [9ndash11] The application of softcomputing techniques has been observed in work processesinvolving environmental control [4 5] weed control pestand biological processes [12] and greenhouse climate control[9 13]
The work presented in [14] is a simulator that aimsto provide a model of irrigation scheduling consideringdifferent times and different distributions of water in sensitiveplant species lack thereof Project development provides thebiophysical modeling technical and decision subsystemsTime schedule irrigation is carried out considering the watershortage the amount of available soil water for lawn andplant composition The limitation of this study is that it is asimulated process and not done in a real environment
The work in [15] shows an irrigation system used forornamental plants of different types calculating irrigationfrom soil type This work uses crop coefficient informationthe type of soil cultivation wind speed solar radiation tem-perature relative humidity and rainfall level obtained froma weather station and other measuring devices purchased forthat purpose An important limitation is the use of expensivemeasuring instruments applied to field crops to which mostfarmers do not have an access
The work in [16] integrates fuzzy logic and neural net-works in a network of sensors in precision irrigation schemeThis is a work in software which simulates the behavior ofirrigation scheduling and is expected to have favorable resultsin manual application One drawback is that it proposes theuse of precision irrigation equipment (little affordable inmostof the time for a producer) to obtain information in a realenvironment Blurring the crop is also observed althoughsome environmental variables needed for irrigation controlsuch as humidity and temperature which are integratedvariables and factors inherent at the crop are ignored
In this study the development of a smart irrigation systembased on microclimate variables such as temperature andrelative humidity in a shadow mesh structure is proposedThis system is designed to improve irrigation scheme forhabanero pepper (Capsicum chinense Jacq) using the fewestnumber of variables and taking advantage of expert knowl-edge A relevant aspect of this proposal is the possibility
Traditional irrigation scheme
Fuzzy irrigation scheme
Figure 1 Irrigation schemes
of implementation in crops where farmers do not havesufficient financial resources and therefore less access to thetechnification
2 Materials and Methods
This work was carried out in a metal covered roof infras-tructure with milky white plastic with 50 and side wallsfront and back with mesh antiaphids in an area of 4824m2(134mtimes 36m) inTechnological Institute of Conkal Yucatanlocated at Km 163 of the Merida-Motul road Geographicalcoordinates are 21∘ 361015840 north latitude 19∘ 321015840 south 87∘321015840 east 90∘ 251015840 west longitude at an altitude of 9 metersaccording to INEGI Geostatistical Framework (2010) Theclimate at the experimental site according to the classificationof Koppenmodified byGarcia [17] is the Awo (1199091015840) (1198941015840) 119892 typeIn the shade house when the seedlings had about 10 cm tallthey were transplanted in containers with compost
For the growing crop two irrigation schemes were em-ployed a traditional scheme and a fuzzy scheme (Figure 1)
Traditional irrigation scheme was performed using thefollowing criteria
(1) obtaining data through an Class A evaporation pan(2) applying data crop coefficient (119870
119888) for habanero
pepper (Tables 1(a) and 1(b))(3) calculating crop evapotranspiration (ET)(4) calculating irrigation depth(5) calculating the volume of water to be applied to
replace the water lost by evapotranspiration(6) calculating irrigation time
ETo calculation (reference evapotranspiration) can be esti-mated from the Class A evaporation pan The crop coeffi-cient (119870
119888) was determined by relating the measured crop
evapotranspiration (ETc) with ETo calculated ET is evapo-transpiration (combined loss of water to plant transpirationand soil evaporation) The irrigation depth is calculated todetermine the amount ofwater thatmust be applied to the soilto satisfy the needs of the cropThe calculation of the volumeof water is obtained for estimating evapotranspiration lossesand irrigation time is determined by considering the methodof irrigation and type of crop
International Journal of Distributed Sensor Networks 3
Initial Value for soilMoistureREPEAT
WHILE soilMoisture gt 70READ Analog Port Soil Moisture Sensor ValueCALCULATE soilMoisture based on VH400 eqWAIT 10 minutes
endWHILECALL Fuzzy Irrigation Time for Habanero pepper (INPUT RelativeHumidity Temperature OUTPUT IrrigationTime)WHILE IrrigationTime gt 0
WRITE Digital Value for pump activationCALCULATE IrrigationTime countdown
endWHILEWRITE Digital Value for pump deactivate
UNTIL (true)
Algorithm 1 Fuzzy irrigation process pseudocode
Table 1 (a) Crop coefficient for habanero pepper (60) (b) Cropcoefficient for habanero pepper (100)
(a)
Crop growth () Days after transplanting 119870119888
On the other hand fuzzy irrigation schema is proposedThe proposed system consists of four sections first measur-ing and estimating the volume of water lost subsequentlymeasuring level categorization through measuring the rela-tive humidity and temperature further the integration of thecommunication system and finally a fuzzy mechanism forthe estimation of irrigation
21 Measuring and Estimating the Volume of Water The soilused was characterized to obtain the parameters of Table 2 toidentify themoisture levels percentageWith these propertiesfield capacity is obtained which is the volume of water that
Table 2 Soil properties
Physical properties UnitApparent density 093 gsdotcmminus3
Wilting point 173Field capacity 558Chemical properties UnitPh 588CE 385 dSmminus1
is capable of retaining the soil of 14 MLCMminus2 Once knownfield capacity using traditional irrigation scheme a volumebetween 70 and 80 range is chosen to make growingHabanero pepper develop properly
The proposed system can be integrated into an algorithm(Figure 2 and Algorithm 1)
To measure the volume the VH400-2M sensor is usedThis element provides a signal from 0 to 3V in accordancewith soil moisture It can be seen that the useful limits of thesensor are 50 (Figure 3)
The interpolation of the measured data results in
119881 = 42times 10minus51198671198783 minus 000381198671198782 + 01265119867119878
+ 00888(1)
Equation (1) can be simplified for the range of interest inorder to reduce processing time as indicated in the sensordata sheet resulting in the linear relation (2)This equation isused to convert the voltage in a soil moisture value
119867119878 = 2632119881minus 789 (2)
In this way with this sensor the estimated lost soil moistureis obtained
22 Measurement of Relative Humidity and TemperatureThe measurement of relative humidity and temperature isperformed by a sensor DHT11This sensor is characterized bythe calibrated digital signal It consists of two resistive sensors
4 International Journal of Distributed Sensor Networks
Table 3 Rules of the fuzzy system in the growth stage of the crop
TemperatureVery few Few Medium High Very high
HumidityVery few Medium Medium Long Long Very longFew Medium Medium Medium Medium LongMedium Short Short Short Short MediumHigh Very short Very short Very short Very short Very shortVery high Very short Very short Very short Very short Very short
Start
Measurement and estimation ofthe volume of water lost
Yes Is it in theoptimal range
No
NoThe volume isbelow 70
Yes
Valuation of thevolume trend
Irrigation isscheduled
No
Yes
Fuzzy mechanism forestimating irrigation time
Irrigationrun
Yes Irrigationended No
Figure 2 Algorithm that incorporated the fuzzy irrigation process
(NTC and humidity) and a small 8-bit microcontroller It canmeasure the humidity in the range 20 approximately 95and the temperature range between 0∘C and 50∘C Protocoluses 1-wire communication and the size is small and has lowpower consumption and the ability to transmit the signal upto 20 meters away
23 Fuzzy System
231 Fuzzy Rules A model for the climate behavior is toocomplex and the uncertainty is always present so the use ofthe fuzzy system in order to program an irrigation schemeis proposed The fuzzy system uses the expert knowledge inform of rules to control the aperture time of the valve andtherefor the water supplied The knowledge base from the
expert provides the information necessary to perform thedesign of the irrigation program sequence as well as thegeneration of the rules that are part of the fuzzy inferencemechanism (see Table 3)
The proposed model employs Mamdani fuzzy mecha-nism described with MISO type fuzzy rules shown on thesystem equation
1198771 If 1199061 is 1198601198951 1199062 is 1198601198962 119906119899 is 119860
119897
119899then 1199101 is 1198611199031
119877119895 If 1199061 is 1198601198951 1199062 is 1198601198962 119906119899 is 119860
119897
119899then 119910
119895is 119861119903119895
119877119898 If 1199061 is 1198601198951 1199062 is 1198601198962 119906119899 is 119860
119897
119899then 119910
119898is 119861119905119898
(3)
International Journal of Distributed Sensor Networks 5
35
30
25
20
15
10
05
0100 20 30
Out
put v
olta
ge (V
)
40 50 60
Soil moisture ()
y = 42E minus 5x3 minus 00038x2 + 01265x + 00888R2 = 09974
Figure 3 Humidity sensor characterization
where 119860119895119894 119860119896
2 119860
119897
119899and 119861119904
119895for 119877119895 represent the corre-
sponding linguistic values [18]The processing block consistsof a hardware Arduino microcontroller which is the Atmel328p where the irrigation program established by the fuzzyrules is stored
232 Fuzzy Sets The linguistic variables to form the fuzzysets are as follows
(i) Temperature Due to the warm air in the shadow houseinfrastructure is retained and an important consideration inthe cultivation under protected conditions is the temperaturefactor because it favors the evaporation of water and has animportant impact in the crop The low technification of thisstructure only can provide air movement through the roofdoors or the antiaphid mesh around of him
(ii) RelativeHumidityThe humidity factor has great influenceon the crop Excess moisture in habanero pepper plantsaffects its development so it should not be given water to thecrop when the humidity is high however when the humiditydecreases it is necessary to supply water to the plant
(iii) Stages of the Crop Growth stages (119870119888) influence the devel-
opment of the plant especially considering that the habaneropepper is a plant that requires large amounts of water whichultimately affects the quality of the fruit This is an importantfactor for the habanero pepper because to obtain designationof origin you must have certain characteristics of the fruit atharvest
(iv) Irrigation Time The output of the fuzzy system is theirrigation time and represents the run time in minutes Thefrequency of watering time keeps the amount of water neededto avoid crop water stress
In order to convert the linguistic variables on fuzzynumbers this paper proposes the calculation of intermediatevalues of the linguistic range through the triangular function(4) while outliers are modeled by the Gamma function left
10
30 53 75 98 120
05
Growth
MaturityDevelopment
MaxMin
Figure 4 Input sets for the variable stage of development
part of the linguistic rang (5) and 119871 function right part of thelinguistic range (6)
120583 (119909 119886119898 119887)
= max min (119909 minus 119886) (119898 minus 119886) minus 1 (119887 minus 119909) (119887 minus 119898) minus 1 0 (4)
120583 (119909) =
0 119909 le 119886
(119909 minus 119886) (119898 minus 119886)minus1119909 isin (119886119898)
1 119909 ge 119898
(5)
The membership function is created with expert knowledgeand adopts a graphic form determined according to the typeof value associated with the fuzzy set The 119871 and Gammafunctions correspond to the membership functions whichare used to calculate extreme fuzzy values Therefore the 119871function is defined by
119871 = 1minusGamma (6)
The graphic form of the linguistic variables defined by thelast equations is shown in Figures 4 5 6 and 7
233 Inference Mechanism The inference mechanism hastwo basic tasks determines the relevance and extent of eachrule in relation to the current entries (119906
119894) and generates the
corresponding conclusions The combination of the sets ofrules with inputs can be calculated as follows
6 International Journal of Distributed Sensor Networks
10
05
130 175 220 265 310 355 400
MaxMin
Medium
Very fewFew Very high
High
Figure 5 Input sets for the variable relative humidity
10
05
300 4166 7666 8833 10005333 650
MaxMin
Medium
Very fewFew Very high
High
Figure 6 Input sets for the variable temperature
The result is a set with the ldquofiredrdquo rules To obtain a crispvalue that can be applied to the valve it will be necessary tocalculate the center of the graph and this can be done by
119910 =
int119910120583 (119910) 119910 119889119910
int119910120583 (119910) 119910 119889119910
(8)
24 Data Acquisition System The proposed communicationsystem includes a basic structure that can be used in agricul-tural environments and this proposal aims to organize thesections of a sensor network and facilitates the selection oftechnologies required to implement the ZigBee networkThecommunication system uses IEEE802154 protocol (ZigBee)which was implemented on an Arduino board with XBee Promodule of Maxtream configured with a PAN ID 3332 a rateof 9600 baud 8 data bits and no parity (see Figure 8)
The structure presented is divided into two sections aninternal for data collection (sensor network) and an exter-nal that can send information to central computers to storeandor process informationThe internal section is composedof elements that collect information from the agriculturalparameters of interest such as temperature relative humidityand soil moisture (sensors) the data collected will be sent todevices for processing through the ZigBee protocol which
10
05
0 333 667 100 1333 1667 200
Medium
Very shortShort Very long
Long
MaxMin
Figure 7 Output sets for the variable irrigation time
Network PAN id = 3332
CoordinatorDH 0DL FFFFMY 1
Fuzzy segmentDH 0DL 1MY A
Traditional segmentDH 0DL 1MY B
Figure 8 Sensor network
is used for transmission (end devices) The internal sectioncan be implemented through Arduino boards with Xbeemodules In the transmission of information the router nodewill receive the information of the end devices and will betransmitted to the coordinator for central processing in orderto be able to connect to a larger networkThe external sectionis composed of central processing devices sending data toremote nodes via Ethernet WiFi mobile devices or othermeans that can send information to other locations as shownin Figure 9
The data acquisition is made with a DuemilanovaArduino and this platform is programed for a sampling rateof 5 minutes Each value from the corresponding sensor hasan header for identification as shown in Algorithm 2 Thesedata are linked with the Xbee devices for the transmission tothe other Xbee configured like a coordinator
To get data from the serial port that the Xbee Shield(Coordinator) is sending we programmethods as can be seenin Algorithm 3 and the header of each data frame is definedso that it recognizes and takes the indicated action for storageat the database every time that receives data (Algorithm 4)
3 Results and Discussion
The behavior of the proposed system was supervised fromMarch to May 2013 this corresponds to all cycle of cropTables 4(a) 4(b) and 4(c) show the average values acquiredfrom the system The results obtained are compared with
International Journal of Distributed Sensor Networks 7
Shaded houseinfrastructure
Sensors
Sensors
SensorsSensors
SensorsSensors
External section
ZigBee
ZigBeeZigBee
coordinator
end device
ZigBeeend device
Internal section
router
GPRS Ethernet WiFi
Centralprocessing
Arduino + Xbee
Arduino + Xbee
50m
Figure 9 Internal and external sections
65432100 10 20 30 40 50 60 70
Days after transplant
Etc (
mm
)
Figure 10 Evapotranspiration values for the crop cycle
the results of calculations performed for the water loss byevapotranspiration reference (Eto) as shown in the work ofPerez-Gutierrez et al [19] The comparison between fuzzysystems of irrigation and traditional irrigationwas performedto validate the data obtained
The Etc shows values that are in an interval of 3mmsdotdayminus1(Figure 10) This range is stable for all the harvest period ifvariations of the Etc were more spacious and then shouldtake into account a greater number of variables thereforeby maintaining a controlled environment with the use of theshadow house we can use a small number of sensors and inthis proposal the use of three of them is enough to estimatethe irrigation
The system showed a deviation of +011 in Marchminus039 in April and +051 in May (Figures 11 12 and13) The supplied volume by the fuzzy system is comparedwith the calculated volume resulting in a small deviationtherefore we can establish the reliability of the system
0500
10001500200025003000350040004500
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
Volu
me (
L)
Days (March)
Calculated volume (L)Supplied volume (L)
Figure 11 Comparison between traditional irrigation and the fuzzysystem (March 2013)
With the fuzzy system validated the analysis of the datafor eachmonth can be made As part of the validation sampleMarch 20 is shown In Figure 14 the frequency of irrigationduring the day is graphed
According to the data obtained with respect to thefrequency of irrigation Figure 14 shows that the frequencyof irrigation is higher in the time period between 1030and 1300 in this period we can observe an increase trendin the temperature (Figure 15) and a low relative humidity(Figure 16) indicating a water accumulation in the soiltherefore a balance between evapotranspiration and thewater applied to the crop is generated
8 International Journal of Distributed Sensor Networks
Table4(a)D
atafrom
them
onth
ofMarch
forthe
cycle
ofcrop(b)
Datafrom
them
onth
ofAp
rilforthe
cycle
ofcrop(c)Datafrom
them
onth
ofMay
forthe
cycle
ofcrop
(a)
March
Day
Etc(mmsdotdıaminus1 )
FuzzyIrrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
Day
Etc
(mmsdotdıaminus1)
Fuzzy
Irrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy
(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
2258425473
1341340
92235568182
2029668918
17357783512
168119
182801986364
2810
025131
3257725364
10913627
1818
937879
2024170272
18307273124
14404782
240079697
2413
317471
437044
9566
15902236
2650372727
2909504
086
19373218858
17796109
2966018182
2931254056
5384566297
182036
3033933333
3020376637
2039600
9363
18436127
3072
687879
3110
250267
6333154632
149705
2495
083333
2616
59036
2134154618
15833791
2638965152
2682497426
7307303393
14569518
2472
201515
2413
555204
22444
59937
22096245
3682707576
3491
875286
837064
8946
17890227
2981704545
2911070015
23450743937
2116
9736
3528289394
3540134599
93627264
2817962364
2993
727273
2848846
704
24467591342
21811636
3635272727
3672
453809
10383003911
1852
4945
3174
921212
3008105683
2513
804632
566
89909
9448318182
1084213265
112655606
8411413364
1902227273
2085708733
26096752606
45595818
759930303
7598931903
1224746
8837
11749273
1999
775758
1943615697
27366104898
17867809
2977
968182
287538114
113
231084615
1112
2518
185375303
1814
934321
28346
753609
1576
0455
2626742424
2723396
476
14242255034
10806
673
1801112121
1902666591
29460879854
22293755
3715
625758
3619
741905
15349001336
1674
8164
2791
3606
062741050084
30462318448
21561836
3593
639394
3631040
596
16369959684
17351655
2891
942424
2905656565
31488327963
22941936
3823656061
3835318856
(b)
April
Day
Etc(mmsdotdıaminus1)
FuzzyIrrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
Day
Etc
(mmsdotdıaminus1)
FuzzyIrrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
13961718653
18771482
3128580303
3111526554
16399938767
17371336
2895
222727
31411117
32
4169559209
25227564
4204593939
3274
764145
17409686903
192428
3207133333
3217
673412
3404
8530602
18909436
3151572727
3179
708499
18471907325
21807836
3634639394
370635146
44
3418113126
15290055
2548342424
2684579772
195057651274
22931773
3821962121
3972
270022
54344343997
20729182
3454863636
3412
039796
203687610895
14040
036
23400
06061
2896
242824
652164
59781
24479782
4079
963636
4096
997931
214583233577
21846
064
364101060
63599
663234
75021289634
23754855
3959142424
3943711656
223708343758
17238536
2873
089394
2912
526377
84818401971
25410745
4235124242
3784364
058
235197175777
24660
44110
0666
6740
8185231
94929600332
2337
8527
3896
421212
3871699047
245013674745
23637727
3939621212
3937730937
104425304
818
2076
9664
346161060
63475
626276
25460
4622494
2139
2809
3565468182
3616
46205
1143046
8744
720112073
3352012121
3380893615
264715697984
22946236
3824372727
3703700535
12303026964
214473727
2412
287879
2379
968211
274767017231
2235
9845
37266
40909
37440
06578
International Journal of Distributed Sensor Networks 9
(b)Con
tinued
April
Day
Etc(mmsdotdıaminus1)
FuzzyIrrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
Day
Etc
(mmsdotdıaminus1)
FuzzyIrrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
134228506725
1981396
43302327273
3321061416
2849999503
2313
4864
385581060
63926951783
14419946964
121049973
3508328788
3298
255743
294685183652
2216
5509
3694
251515
3679
734635
1541340
0889
18688127
3114
687879
3246842989
303708190798
17870818
2978
469697
2912
406242
(c)
May
Day
Etc(mmsdotdıaminus1)
FuzzyIrrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
Day
Etc
(mmsdotdıaminus1)
Fuzzy
Irrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy
(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
134840
0241
16046282
2674
3803
273632909
14281323798
12056146
200935758
22095119
42
428945109
19954027
332567121
336892701
15345795001
15992027
266533788
2715
86759
3364
868417
17514855
2919
14242
286566985
16376349121
17962409
2993
73485
295583908
4400339841
190312
317186667
314426176
17341760316
1610
1982
268366364
268417925
527064
0388
1241364
62068940
912125604
6418
343767282
15971582
26619303
2699
94192
6351451778
16295346
2715
89091
276029581
19313612887
15119536
2519
92273
243822456
7329792176
1609100
9268183485
2590
1817
20293326251
14367546
2394
59091
246310986
8335111737
15496273
258271212
263196142
21305215788
13911227
2318
53788
230377899
9358129742
16387591
273126515
2812
7444
222
304
887854
14010273
233504545
2397
15919
10365825019
1766
4609
294410152
287318298
23252884747
1503564
62505940
912394
58361
11377619727
17437236
2906206
0629658184
24252884747
113733
1895
55198615216
12373897867
17908982
29848303
293658698
25320855932
1554804
62591
34091
2519
99659
13283378267
1312
6791
218779848
222564771
10 International Journal of Distributed Sensor Networks
Calculated volume (L)Supplied volume (L)
0500
100015002000250030003500400045005000
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Volu
me (
L)
Days (April)
Figure 12 Comparison between traditional irrigation and the fuzzysystem (April 2013)
Calculated volume (L)Supplied volume (L)
0500
1000150020002500300035004000
1 3 5 7 9 11 13 15 17 19 21 23 25
Volu
me (
L)
Days (May)
Figure 13 Comparison between traditional irrigation and the fuzzysystem (May 2013)
These results establish the reliability of the proposedsystem to provide adequate water and maintain optimummoisture level with low relative humidity to obtain thedesirable quality of hbanero pepper production
In thework ofOrtiz et al [20] the percentage of optimumsoil moisture for growing good quality fruits is 60 the fuzzysystem employs the percentage of 70 indicated by the expertso that it is possible to also set the reliability in the finalquality of the fruit The work presented by Perez-Gutierrezet al [19] mentioned that the volume of water applied to80 of potential evapotranspiration generates the amount ofwater in the soil to favor a constant process of transpirationfruit yield and improvement of water use this requires awater volume 2223m3 haminus1 however the fuzzy system onlyrequires 44995m3 haminus1 which represents a saving of 798water
The proposed system is a better form to schedule irriga-tion unlike Yao et al [16] the objective is to give to the poorcommunities a form to harvest a high demand product likehabanero pepper (with designation of origin) with enoughtechnologic structure The work of Yao et al [16] is using aneural network to refine the sprinkle time based on a patternthat is not described in the paper in a real environmentespecially in Yucatan Mexico the humidity and temperaturevariables have to be considered to obtain a quality product
02468
101214161820
000 224 448 712 936 1200 1424 1648 1912
Dur
atio
n of
irrig
atio
n (m
in)
Hours of day
Figure 14 Irrigation frequency using the fuzzy system (sample of20 March 2013)
40
35
30
25
20
15
10
5
0600 824 1048 1312 1536 1800
Hours of day
Tem
pera
ture
(∘C)
Figure 15 Temperature measurement (sample of 20 March 2013)
1009080706050403020100600 824 1048
Hours of day1312 1536 1800
Tem
pera
ture
(∘C)
Figure 16 Measurement of relative humidity (sample of 20 March2013)
and unlike the work of Yao et al [16] in this work are takeninto account in the fuzzy model and in the expert knowledgeThe use of a neural network is not a significant contributionfor managing the time of irrigation on a shadow housebecause there are no other important variables included andin the Yao et al [16] proposal only the soil moisture and theerror are considered to set the irrigation time
4 Conclusions
This paper has presented a fuzzy system that manages awireless irrigation scheme through an algorithm that takesinto account the conditions of microclimate of a shadowhouse used for growing habanero pepper with designation oforigin in the state of Yucatan The water volume consumed
International Journal of Distributed Sensor Networks 11
float h = dhtreadHumidity( )
float t = dhtreadTemperature()
revisa si retorna un valor valido de lo contrario hay un error
if (isnan (t) || isnan (h)) (
Serialprintln (Failed to read from DHT)
) else (
String hume = Humedad
Serialprintln (hume + h + de Humedad)
String tempe = Temperatura
Serialprintln (tempe + t + Grados Centrigrados )
)
Algorithm 2 Section of the Arduino code
SerialPort conectar = new SerialPort (COM4 9600)
conectarOpen( )
while ( shouldStop)
if (conectarBytesToRead gt 0)
string t = conectarReadLine( )
string h = conectarReadLine( )
string m = conectarReadLine( )
string texto
t = tReplace(r )
resp textText = t
hume textText = h
mov textText = m
if (tStartWith(Temperatura ))
Texto = t
t = tReplace(Temperatura )
else resp textText =
if (hStartWith(Humedad ))
texto = h
h = hReplace(Humedad )
else hume textText =
Algorithm 3 Application connection
according to the graphic that shows the comparison betweenthe volume of water from the system and the manual ortraditional volume gives us a difference of 798 over the bestresult shown in Perez-Gutierrez et al [19] which shows thatthe system can efficiently manage water and that incorpora-tion of the expert knowledge in fuzzy rules allows irrigationscheme without using the pan evaporation or calculating thevolume associated with irrigation as they can be automat-ically supplied without the crop being adversely affected inits development Saving water is about 1524823m3 for a cropof 1000 plants in an 85-day cycle causing transplantation atthe 30th day The microclimate conditions generated by theinfrastructure of shadow house are not exploited by the lack
of modernization that is a producer who does not have theability to use technology to irrigate their crops generally onlyconsidered during the morning or afternoon which doesnot guarantee the product quality since plants could fall ineither water stress or excess moisture However the proposedsystem allows for irrigation supply data from three commonsensors (temperature relative humidity and soil moisture)allowing similarly for reducing infrastructure costs
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
12 International Journal of Distributed Sensor Networks
public void bd tempe( )
SqlConnection con = new SqlConnection()
conConnectionString = Data Source=DAVIDSERVERSQLEXPRESS
Initial Catalog=ARDUINOIntegrated Security=True
try
conOpen( )
catch (SqlException ex)
MessageBoxShow(exMessage)
throw
string tiempo = DateTimeNowToString( )
SqlCommand instruccion = conCreateCommand( )
instruccionCommandText = insert into tempe(horatemperatura)
The authors gratefully acknowledge the support of the Tech-nological Institute of Conkal ofMexico and Prometeo Projectof the Secretariat for Higher Education Science Technologyand Innovation of the Republic of Ecuador and CYTEDnetwork 514RT0486
References
[1] FAOSTAT FAO Statistical Databases amp Data-Sets Food andAgriculture Organization of the United Nations 2012 (Consul-tado Dic 2013) httpfaostatfaoorgsite567DesktopDefaultaspxPageID=567ancor
[2] SIAP Agri-Food and Fishing Information and Statistics ServiceSAGARPA Anuario estadıstico de la Produccion AgrıcolaMexico City Mexico 2011
[3] USDA and FAS ldquoGreenhouse and shade house production tocontinue increasingrdquo GAIN Report MX0024 vol 22 USDAFAS Mexico DF Mexico 2010
[4] H Turral M Svendsen and J M Faures ldquoInvesting in irriga-tion reviewing the past and looking to the futurerdquo AgriculturalWater Management vol 97 no 4 pp 551ndash560 2010
[5] S Tang Q Zhu X Zhou S Liu and M Wu ldquoA conceptionof digital agriculturerdquo in Proceedings of the IEEE InternationalGeoscience and Remote Sensing Symposium (IGARSS rsquo02) vol5 pp 3026ndash3028 June 2002
[6] L Bacci P Battista and B Rapi ldquoAn integrated method forirrigation scheduling of potted plantsrdquo Scientia Horticulturaevol 116 no 1 pp 89ndash97 2008
[7] R Lopez Lopez R Arteaga Ramırez M A Vazquez Pena ILopez Cruz and I Sanchez Cohen ldquoIndice de estres hıdricocomo un indicador del momento de riego en cultivos agrıcolasrdquoAgricultura Tecnica en Mexico vol 35 no 1 pp 97ndash111 2009
[8] N Livellara F Saavedra and E Salgado ldquoPlant based indicatorsfor irrigation scheduling in young cherry treesrdquo AgriculturalWater Management vol 98 no 4 pp 684ndash690 2011
[9] R Qiu S Kang F Li et al ldquoEnergy partitioning and evapotran-spiration of hot pepper grown in greenhouse with furrow anddrip irrigation methodsrdquo Scientia Horticulturae vol 129 no 4pp 790ndash797 2011
[10] J Casadesus M Mata J Marsal and J Girona ldquoA generalalgorithm for automated scheduling of drip irrigation in treecropsrdquo Computers and Electronics in Agriculture vol 83 pp 11ndash20 2012
[11] C O Akinbile and M S Yusoff ldquoGrowth yield and water usepattern of chilli pepper under different irrigation schedulingand managementrdquo Asian Journal of Agricultural Research vol5 no 2 pp 154ndash163 2011
[12] Y Huang Y Lan S J Thomson A Fang W C Hoffmann andR E Lacey ldquoDevelopment of soft computing and applicationsin agricultural and biological engineeringrdquo Computers andElectronics in Agriculture vol 71 no 2 pp 107ndash127 2010
[13] M Omid M Lashgari H Mobli R Alimardani S Mohtasebiand R Hesamifard ldquoDesign of fuzzy logic control systemincorporating human expert knowledge for combine harvesterrdquoExpert Systems with Applications vol 37 no 10 pp 7080ndash70852010
[14] A Merot and J-E Bergez ldquoIRRIGATE a dynamic integratedmodel combining a knowledge-based model and mechanisticbiophysical models for border irrigation managementrdquo Envi-ronmental Modelling and Software vol 25 no 4 pp 421ndash4322010
[15] S L Davis and M D Dukes ldquoIrrigation scheduling perfor-mance by evapotranspiration-based controllersrdquo AgriculturalWater Management vol 98 no 1 pp 19ndash28 2010
[16] Z Yao G Lou Z XiuLi and Q Zhao ldquoResearch and devel-opment precision irrigation control system in agriculturalrdquoin Proceedings of the International Conference on Computerand Communication Technologies in Agriculture Engineering(CCTAE rsquo10) pp 117ndash120 June 2010
[17] E Garcia Modificaciones al Sistema de Clasificacion Climaticode Koppen Serie de Libros no 6 UNAM Instituto deGeografıaMexico City Mexico 5th edition 2004
International Journal of Distributed Sensor Networks 13
[18] K M Passino S Yurkovich and M Reinfrank Fuzzy Controlvol 42 Addison Wesley Longman Menlo Park Calif USA1998
[19] A Perez-Gutierrez A Pineda-Doporto L Latournerie-Moreno and C Godoy-Avila ldquoNiveles de evapotranspiracionpotencial en la produccion de chile habanerordquo Terra Latino-americana vol 26 no 1 pp 53ndash59 2008
[20] W C Q Ortiz A Perez-Gutierrez L L Moreno C May-LaraE R Sanchez and A J M Chacon ldquoUso de agua potencialhıdrico y rendimiento de chile habanero (Capsicum chinenseJacq)rdquo Revista Fitotecnia Mexicana vol 35 no 2 pp 155ndash1602012
2 International Journal of Distributed Sensor Networks
solutions are required based on advanced techniques andtechnologies to provide a better result in the productionof food [4] In this context the use of soft computingtechniques (such as neural networks genetic algorithmsand fuzzy logic) has been considered as real solutions toproblems related to the effective management of agriculturalresources [5] One such resource is hydric characterizedby irrigation systems and affecting plant growth and henceproduction Due to the above there are proposals that enableautomation and control to carry out this process in thebest way [6 7] These methods adjust the amount of watersupplied according to crop water requirements the amountof water available in the channel and system componentsIn systems that use drip irrigation such information has notbeen considered because the amount of water is controlledin a certain manner [8] However the frequency of irrigationdrip systems still depends on climatic factors soil type andcrop type so that the control and close monitoring remainsnecessary in these systems [9ndash11] The application of softcomputing techniques has been observed in work processesinvolving environmental control [4 5] weed control pestand biological processes [12] and greenhouse climate control[9 13]
The work presented in [14] is a simulator that aimsto provide a model of irrigation scheduling consideringdifferent times and different distributions of water in sensitiveplant species lack thereof Project development provides thebiophysical modeling technical and decision subsystemsTime schedule irrigation is carried out considering the watershortage the amount of available soil water for lawn andplant composition The limitation of this study is that it is asimulated process and not done in a real environment
The work in [15] shows an irrigation system used forornamental plants of different types calculating irrigationfrom soil type This work uses crop coefficient informationthe type of soil cultivation wind speed solar radiation tem-perature relative humidity and rainfall level obtained froma weather station and other measuring devices purchased forthat purpose An important limitation is the use of expensivemeasuring instruments applied to field crops to which mostfarmers do not have an access
The work in [16] integrates fuzzy logic and neural net-works in a network of sensors in precision irrigation schemeThis is a work in software which simulates the behavior ofirrigation scheduling and is expected to have favorable resultsin manual application One drawback is that it proposes theuse of precision irrigation equipment (little affordable inmostof the time for a producer) to obtain information in a realenvironment Blurring the crop is also observed althoughsome environmental variables needed for irrigation controlsuch as humidity and temperature which are integratedvariables and factors inherent at the crop are ignored
In this study the development of a smart irrigation systembased on microclimate variables such as temperature andrelative humidity in a shadow mesh structure is proposedThis system is designed to improve irrigation scheme forhabanero pepper (Capsicum chinense Jacq) using the fewestnumber of variables and taking advantage of expert knowl-edge A relevant aspect of this proposal is the possibility
Traditional irrigation scheme
Fuzzy irrigation scheme
Figure 1 Irrigation schemes
of implementation in crops where farmers do not havesufficient financial resources and therefore less access to thetechnification
2 Materials and Methods
This work was carried out in a metal covered roof infras-tructure with milky white plastic with 50 and side wallsfront and back with mesh antiaphids in an area of 4824m2(134mtimes 36m) inTechnological Institute of Conkal Yucatanlocated at Km 163 of the Merida-Motul road Geographicalcoordinates are 21∘ 361015840 north latitude 19∘ 321015840 south 87∘321015840 east 90∘ 251015840 west longitude at an altitude of 9 metersaccording to INEGI Geostatistical Framework (2010) Theclimate at the experimental site according to the classificationof Koppenmodified byGarcia [17] is the Awo (1199091015840) (1198941015840) 119892 typeIn the shade house when the seedlings had about 10 cm tallthey were transplanted in containers with compost
For the growing crop two irrigation schemes were em-ployed a traditional scheme and a fuzzy scheme (Figure 1)
Traditional irrigation scheme was performed using thefollowing criteria
(1) obtaining data through an Class A evaporation pan(2) applying data crop coefficient (119870
119888) for habanero
pepper (Tables 1(a) and 1(b))(3) calculating crop evapotranspiration (ET)(4) calculating irrigation depth(5) calculating the volume of water to be applied to
replace the water lost by evapotranspiration(6) calculating irrigation time
ETo calculation (reference evapotranspiration) can be esti-mated from the Class A evaporation pan The crop coeffi-cient (119870
119888) was determined by relating the measured crop
evapotranspiration (ETc) with ETo calculated ET is evapo-transpiration (combined loss of water to plant transpirationand soil evaporation) The irrigation depth is calculated todetermine the amount ofwater thatmust be applied to the soilto satisfy the needs of the cropThe calculation of the volumeof water is obtained for estimating evapotranspiration lossesand irrigation time is determined by considering the methodof irrigation and type of crop
International Journal of Distributed Sensor Networks 3
Initial Value for soilMoistureREPEAT
WHILE soilMoisture gt 70READ Analog Port Soil Moisture Sensor ValueCALCULATE soilMoisture based on VH400 eqWAIT 10 minutes
endWHILECALL Fuzzy Irrigation Time for Habanero pepper (INPUT RelativeHumidity Temperature OUTPUT IrrigationTime)WHILE IrrigationTime gt 0
WRITE Digital Value for pump activationCALCULATE IrrigationTime countdown
endWHILEWRITE Digital Value for pump deactivate
UNTIL (true)
Algorithm 1 Fuzzy irrigation process pseudocode
Table 1 (a) Crop coefficient for habanero pepper (60) (b) Cropcoefficient for habanero pepper (100)
(a)
Crop growth () Days after transplanting 119870119888
On the other hand fuzzy irrigation schema is proposedThe proposed system consists of four sections first measur-ing and estimating the volume of water lost subsequentlymeasuring level categorization through measuring the rela-tive humidity and temperature further the integration of thecommunication system and finally a fuzzy mechanism forthe estimation of irrigation
21 Measuring and Estimating the Volume of Water The soilused was characterized to obtain the parameters of Table 2 toidentify themoisture levels percentageWith these propertiesfield capacity is obtained which is the volume of water that
Table 2 Soil properties
Physical properties UnitApparent density 093 gsdotcmminus3
Wilting point 173Field capacity 558Chemical properties UnitPh 588CE 385 dSmminus1
is capable of retaining the soil of 14 MLCMminus2 Once knownfield capacity using traditional irrigation scheme a volumebetween 70 and 80 range is chosen to make growingHabanero pepper develop properly
The proposed system can be integrated into an algorithm(Figure 2 and Algorithm 1)
To measure the volume the VH400-2M sensor is usedThis element provides a signal from 0 to 3V in accordancewith soil moisture It can be seen that the useful limits of thesensor are 50 (Figure 3)
The interpolation of the measured data results in
119881 = 42times 10minus51198671198783 minus 000381198671198782 + 01265119867119878
+ 00888(1)
Equation (1) can be simplified for the range of interest inorder to reduce processing time as indicated in the sensordata sheet resulting in the linear relation (2)This equation isused to convert the voltage in a soil moisture value
119867119878 = 2632119881minus 789 (2)
In this way with this sensor the estimated lost soil moistureis obtained
22 Measurement of Relative Humidity and TemperatureThe measurement of relative humidity and temperature isperformed by a sensor DHT11This sensor is characterized bythe calibrated digital signal It consists of two resistive sensors
4 International Journal of Distributed Sensor Networks
Table 3 Rules of the fuzzy system in the growth stage of the crop
TemperatureVery few Few Medium High Very high
HumidityVery few Medium Medium Long Long Very longFew Medium Medium Medium Medium LongMedium Short Short Short Short MediumHigh Very short Very short Very short Very short Very shortVery high Very short Very short Very short Very short Very short
Start
Measurement and estimation ofthe volume of water lost
Yes Is it in theoptimal range
No
NoThe volume isbelow 70
Yes
Valuation of thevolume trend
Irrigation isscheduled
No
Yes
Fuzzy mechanism forestimating irrigation time
Irrigationrun
Yes Irrigationended No
Figure 2 Algorithm that incorporated the fuzzy irrigation process
(NTC and humidity) and a small 8-bit microcontroller It canmeasure the humidity in the range 20 approximately 95and the temperature range between 0∘C and 50∘C Protocoluses 1-wire communication and the size is small and has lowpower consumption and the ability to transmit the signal upto 20 meters away
23 Fuzzy System
231 Fuzzy Rules A model for the climate behavior is toocomplex and the uncertainty is always present so the use ofthe fuzzy system in order to program an irrigation schemeis proposed The fuzzy system uses the expert knowledge inform of rules to control the aperture time of the valve andtherefor the water supplied The knowledge base from the
expert provides the information necessary to perform thedesign of the irrigation program sequence as well as thegeneration of the rules that are part of the fuzzy inferencemechanism (see Table 3)
The proposed model employs Mamdani fuzzy mecha-nism described with MISO type fuzzy rules shown on thesystem equation
1198771 If 1199061 is 1198601198951 1199062 is 1198601198962 119906119899 is 119860
119897
119899then 1199101 is 1198611199031
119877119895 If 1199061 is 1198601198951 1199062 is 1198601198962 119906119899 is 119860
119897
119899then 119910
119895is 119861119903119895
119877119898 If 1199061 is 1198601198951 1199062 is 1198601198962 119906119899 is 119860
119897
119899then 119910
119898is 119861119905119898
(3)
International Journal of Distributed Sensor Networks 5
35
30
25
20
15
10
05
0100 20 30
Out
put v
olta
ge (V
)
40 50 60
Soil moisture ()
y = 42E minus 5x3 minus 00038x2 + 01265x + 00888R2 = 09974
Figure 3 Humidity sensor characterization
where 119860119895119894 119860119896
2 119860
119897
119899and 119861119904
119895for 119877119895 represent the corre-
sponding linguistic values [18]The processing block consistsof a hardware Arduino microcontroller which is the Atmel328p where the irrigation program established by the fuzzyrules is stored
232 Fuzzy Sets The linguistic variables to form the fuzzysets are as follows
(i) Temperature Due to the warm air in the shadow houseinfrastructure is retained and an important consideration inthe cultivation under protected conditions is the temperaturefactor because it favors the evaporation of water and has animportant impact in the crop The low technification of thisstructure only can provide air movement through the roofdoors or the antiaphid mesh around of him
(ii) RelativeHumidityThe humidity factor has great influenceon the crop Excess moisture in habanero pepper plantsaffects its development so it should not be given water to thecrop when the humidity is high however when the humiditydecreases it is necessary to supply water to the plant
(iii) Stages of the Crop Growth stages (119870119888) influence the devel-
opment of the plant especially considering that the habaneropepper is a plant that requires large amounts of water whichultimately affects the quality of the fruit This is an importantfactor for the habanero pepper because to obtain designationof origin you must have certain characteristics of the fruit atharvest
(iv) Irrigation Time The output of the fuzzy system is theirrigation time and represents the run time in minutes Thefrequency of watering time keeps the amount of water neededto avoid crop water stress
In order to convert the linguistic variables on fuzzynumbers this paper proposes the calculation of intermediatevalues of the linguistic range through the triangular function(4) while outliers are modeled by the Gamma function left
10
30 53 75 98 120
05
Growth
MaturityDevelopment
MaxMin
Figure 4 Input sets for the variable stage of development
part of the linguistic rang (5) and 119871 function right part of thelinguistic range (6)
120583 (119909 119886119898 119887)
= max min (119909 minus 119886) (119898 minus 119886) minus 1 (119887 minus 119909) (119887 minus 119898) minus 1 0 (4)
120583 (119909) =
0 119909 le 119886
(119909 minus 119886) (119898 minus 119886)minus1119909 isin (119886119898)
1 119909 ge 119898
(5)
The membership function is created with expert knowledgeand adopts a graphic form determined according to the typeof value associated with the fuzzy set The 119871 and Gammafunctions correspond to the membership functions whichare used to calculate extreme fuzzy values Therefore the 119871function is defined by
119871 = 1minusGamma (6)
The graphic form of the linguistic variables defined by thelast equations is shown in Figures 4 5 6 and 7
233 Inference Mechanism The inference mechanism hastwo basic tasks determines the relevance and extent of eachrule in relation to the current entries (119906
119894) and generates the
corresponding conclusions The combination of the sets ofrules with inputs can be calculated as follows
6 International Journal of Distributed Sensor Networks
10
05
130 175 220 265 310 355 400
MaxMin
Medium
Very fewFew Very high
High
Figure 5 Input sets for the variable relative humidity
10
05
300 4166 7666 8833 10005333 650
MaxMin
Medium
Very fewFew Very high
High
Figure 6 Input sets for the variable temperature
The result is a set with the ldquofiredrdquo rules To obtain a crispvalue that can be applied to the valve it will be necessary tocalculate the center of the graph and this can be done by
119910 =
int119910120583 (119910) 119910 119889119910
int119910120583 (119910) 119910 119889119910
(8)
24 Data Acquisition System The proposed communicationsystem includes a basic structure that can be used in agricul-tural environments and this proposal aims to organize thesections of a sensor network and facilitates the selection oftechnologies required to implement the ZigBee networkThecommunication system uses IEEE802154 protocol (ZigBee)which was implemented on an Arduino board with XBee Promodule of Maxtream configured with a PAN ID 3332 a rateof 9600 baud 8 data bits and no parity (see Figure 8)
The structure presented is divided into two sections aninternal for data collection (sensor network) and an exter-nal that can send information to central computers to storeandor process informationThe internal section is composedof elements that collect information from the agriculturalparameters of interest such as temperature relative humidityand soil moisture (sensors) the data collected will be sent todevices for processing through the ZigBee protocol which
10
05
0 333 667 100 1333 1667 200
Medium
Very shortShort Very long
Long
MaxMin
Figure 7 Output sets for the variable irrigation time
Network PAN id = 3332
CoordinatorDH 0DL FFFFMY 1
Fuzzy segmentDH 0DL 1MY A
Traditional segmentDH 0DL 1MY B
Figure 8 Sensor network
is used for transmission (end devices) The internal sectioncan be implemented through Arduino boards with Xbeemodules In the transmission of information the router nodewill receive the information of the end devices and will betransmitted to the coordinator for central processing in orderto be able to connect to a larger networkThe external sectionis composed of central processing devices sending data toremote nodes via Ethernet WiFi mobile devices or othermeans that can send information to other locations as shownin Figure 9
The data acquisition is made with a DuemilanovaArduino and this platform is programed for a sampling rateof 5 minutes Each value from the corresponding sensor hasan header for identification as shown in Algorithm 2 Thesedata are linked with the Xbee devices for the transmission tothe other Xbee configured like a coordinator
To get data from the serial port that the Xbee Shield(Coordinator) is sending we programmethods as can be seenin Algorithm 3 and the header of each data frame is definedso that it recognizes and takes the indicated action for storageat the database every time that receives data (Algorithm 4)
3 Results and Discussion
The behavior of the proposed system was supervised fromMarch to May 2013 this corresponds to all cycle of cropTables 4(a) 4(b) and 4(c) show the average values acquiredfrom the system The results obtained are compared with
International Journal of Distributed Sensor Networks 7
Shaded houseinfrastructure
Sensors
Sensors
SensorsSensors
SensorsSensors
External section
ZigBee
ZigBeeZigBee
coordinator
end device
ZigBeeend device
Internal section
router
GPRS Ethernet WiFi
Centralprocessing
Arduino + Xbee
Arduino + Xbee
50m
Figure 9 Internal and external sections
65432100 10 20 30 40 50 60 70
Days after transplant
Etc (
mm
)
Figure 10 Evapotranspiration values for the crop cycle
the results of calculations performed for the water loss byevapotranspiration reference (Eto) as shown in the work ofPerez-Gutierrez et al [19] The comparison between fuzzysystems of irrigation and traditional irrigationwas performedto validate the data obtained
The Etc shows values that are in an interval of 3mmsdotdayminus1(Figure 10) This range is stable for all the harvest period ifvariations of the Etc were more spacious and then shouldtake into account a greater number of variables thereforeby maintaining a controlled environment with the use of theshadow house we can use a small number of sensors and inthis proposal the use of three of them is enough to estimatethe irrigation
The system showed a deviation of +011 in Marchminus039 in April and +051 in May (Figures 11 12 and13) The supplied volume by the fuzzy system is comparedwith the calculated volume resulting in a small deviationtherefore we can establish the reliability of the system
0500
10001500200025003000350040004500
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
Volu
me (
L)
Days (March)
Calculated volume (L)Supplied volume (L)
Figure 11 Comparison between traditional irrigation and the fuzzysystem (March 2013)
With the fuzzy system validated the analysis of the datafor eachmonth can be made As part of the validation sampleMarch 20 is shown In Figure 14 the frequency of irrigationduring the day is graphed
According to the data obtained with respect to thefrequency of irrigation Figure 14 shows that the frequencyof irrigation is higher in the time period between 1030and 1300 in this period we can observe an increase trendin the temperature (Figure 15) and a low relative humidity(Figure 16) indicating a water accumulation in the soiltherefore a balance between evapotranspiration and thewater applied to the crop is generated
8 International Journal of Distributed Sensor Networks
Table4(a)D
atafrom
them
onth
ofMarch
forthe
cycle
ofcrop(b)
Datafrom
them
onth
ofAp
rilforthe
cycle
ofcrop(c)Datafrom
them
onth
ofMay
forthe
cycle
ofcrop
(a)
March
Day
Etc(mmsdotdıaminus1 )
FuzzyIrrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
Day
Etc
(mmsdotdıaminus1)
Fuzzy
Irrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy
(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
2258425473
1341340
92235568182
2029668918
17357783512
168119
182801986364
2810
025131
3257725364
10913627
1818
937879
2024170272
18307273124
14404782
240079697
2413
317471
437044
9566
15902236
2650372727
2909504
086
19373218858
17796109
2966018182
2931254056
5384566297
182036
3033933333
3020376637
2039600
9363
18436127
3072
687879
3110
250267
6333154632
149705
2495
083333
2616
59036
2134154618
15833791
2638965152
2682497426
7307303393
14569518
2472
201515
2413
555204
22444
59937
22096245
3682707576
3491
875286
837064
8946
17890227
2981704545
2911070015
23450743937
2116
9736
3528289394
3540134599
93627264
2817962364
2993
727273
2848846
704
24467591342
21811636
3635272727
3672
453809
10383003911
1852
4945
3174
921212
3008105683
2513
804632
566
89909
9448318182
1084213265
112655606
8411413364
1902227273
2085708733
26096752606
45595818
759930303
7598931903
1224746
8837
11749273
1999
775758
1943615697
27366104898
17867809
2977
968182
287538114
113
231084615
1112
2518
185375303
1814
934321
28346
753609
1576
0455
2626742424
2723396
476
14242255034
10806
673
1801112121
1902666591
29460879854
22293755
3715
625758
3619
741905
15349001336
1674
8164
2791
3606
062741050084
30462318448
21561836
3593
639394
3631040
596
16369959684
17351655
2891
942424
2905656565
31488327963
22941936
3823656061
3835318856
(b)
April
Day
Etc(mmsdotdıaminus1)
FuzzyIrrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
Day
Etc
(mmsdotdıaminus1)
FuzzyIrrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
13961718653
18771482
3128580303
3111526554
16399938767
17371336
2895
222727
31411117
32
4169559209
25227564
4204593939
3274
764145
17409686903
192428
3207133333
3217
673412
3404
8530602
18909436
3151572727
3179
708499
18471907325
21807836
3634639394
370635146
44
3418113126
15290055
2548342424
2684579772
195057651274
22931773
3821962121
3972
270022
54344343997
20729182
3454863636
3412
039796
203687610895
14040
036
23400
06061
2896
242824
652164
59781
24479782
4079
963636
4096
997931
214583233577
21846
064
364101060
63599
663234
75021289634
23754855
3959142424
3943711656
223708343758
17238536
2873
089394
2912
526377
84818401971
25410745
4235124242
3784364
058
235197175777
24660
44110
0666
6740
8185231
94929600332
2337
8527
3896
421212
3871699047
245013674745
23637727
3939621212
3937730937
104425304
818
2076
9664
346161060
63475
626276
25460
4622494
2139
2809
3565468182
3616
46205
1143046
8744
720112073
3352012121
3380893615
264715697984
22946236
3824372727
3703700535
12303026964
214473727
2412
287879
2379
968211
274767017231
2235
9845
37266
40909
37440
06578
International Journal of Distributed Sensor Networks 9
(b)Con
tinued
April
Day
Etc(mmsdotdıaminus1)
FuzzyIrrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
Day
Etc
(mmsdotdıaminus1)
FuzzyIrrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
134228506725
1981396
43302327273
3321061416
2849999503
2313
4864
385581060
63926951783
14419946964
121049973
3508328788
3298
255743
294685183652
2216
5509
3694
251515
3679
734635
1541340
0889
18688127
3114
687879
3246842989
303708190798
17870818
2978
469697
2912
406242
(c)
May
Day
Etc(mmsdotdıaminus1)
FuzzyIrrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
Day
Etc
(mmsdotdıaminus1)
Fuzzy
Irrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy
(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
134840
0241
16046282
2674
3803
273632909
14281323798
12056146
200935758
22095119
42
428945109
19954027
332567121
336892701
15345795001
15992027
266533788
2715
86759
3364
868417
17514855
2919
14242
286566985
16376349121
17962409
2993
73485
295583908
4400339841
190312
317186667
314426176
17341760316
1610
1982
268366364
268417925
527064
0388
1241364
62068940
912125604
6418
343767282
15971582
26619303
2699
94192
6351451778
16295346
2715
89091
276029581
19313612887
15119536
2519
92273
243822456
7329792176
1609100
9268183485
2590
1817
20293326251
14367546
2394
59091
246310986
8335111737
15496273
258271212
263196142
21305215788
13911227
2318
53788
230377899
9358129742
16387591
273126515
2812
7444
222
304
887854
14010273
233504545
2397
15919
10365825019
1766
4609
294410152
287318298
23252884747
1503564
62505940
912394
58361
11377619727
17437236
2906206
0629658184
24252884747
113733
1895
55198615216
12373897867
17908982
29848303
293658698
25320855932
1554804
62591
34091
2519
99659
13283378267
1312
6791
218779848
222564771
10 International Journal of Distributed Sensor Networks
Calculated volume (L)Supplied volume (L)
0500
100015002000250030003500400045005000
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Volu
me (
L)
Days (April)
Figure 12 Comparison between traditional irrigation and the fuzzysystem (April 2013)
Calculated volume (L)Supplied volume (L)
0500
1000150020002500300035004000
1 3 5 7 9 11 13 15 17 19 21 23 25
Volu
me (
L)
Days (May)
Figure 13 Comparison between traditional irrigation and the fuzzysystem (May 2013)
These results establish the reliability of the proposedsystem to provide adequate water and maintain optimummoisture level with low relative humidity to obtain thedesirable quality of hbanero pepper production
In thework ofOrtiz et al [20] the percentage of optimumsoil moisture for growing good quality fruits is 60 the fuzzysystem employs the percentage of 70 indicated by the expertso that it is possible to also set the reliability in the finalquality of the fruit The work presented by Perez-Gutierrezet al [19] mentioned that the volume of water applied to80 of potential evapotranspiration generates the amount ofwater in the soil to favor a constant process of transpirationfruit yield and improvement of water use this requires awater volume 2223m3 haminus1 however the fuzzy system onlyrequires 44995m3 haminus1 which represents a saving of 798water
The proposed system is a better form to schedule irriga-tion unlike Yao et al [16] the objective is to give to the poorcommunities a form to harvest a high demand product likehabanero pepper (with designation of origin) with enoughtechnologic structure The work of Yao et al [16] is using aneural network to refine the sprinkle time based on a patternthat is not described in the paper in a real environmentespecially in Yucatan Mexico the humidity and temperaturevariables have to be considered to obtain a quality product
02468
101214161820
000 224 448 712 936 1200 1424 1648 1912
Dur
atio
n of
irrig
atio
n (m
in)
Hours of day
Figure 14 Irrigation frequency using the fuzzy system (sample of20 March 2013)
40
35
30
25
20
15
10
5
0600 824 1048 1312 1536 1800
Hours of day
Tem
pera
ture
(∘C)
Figure 15 Temperature measurement (sample of 20 March 2013)
1009080706050403020100600 824 1048
Hours of day1312 1536 1800
Tem
pera
ture
(∘C)
Figure 16 Measurement of relative humidity (sample of 20 March2013)
and unlike the work of Yao et al [16] in this work are takeninto account in the fuzzy model and in the expert knowledgeThe use of a neural network is not a significant contributionfor managing the time of irrigation on a shadow housebecause there are no other important variables included andin the Yao et al [16] proposal only the soil moisture and theerror are considered to set the irrigation time
4 Conclusions
This paper has presented a fuzzy system that manages awireless irrigation scheme through an algorithm that takesinto account the conditions of microclimate of a shadowhouse used for growing habanero pepper with designation oforigin in the state of Yucatan The water volume consumed
International Journal of Distributed Sensor Networks 11
float h = dhtreadHumidity( )
float t = dhtreadTemperature()
revisa si retorna un valor valido de lo contrario hay un error
if (isnan (t) || isnan (h)) (
Serialprintln (Failed to read from DHT)
) else (
String hume = Humedad
Serialprintln (hume + h + de Humedad)
String tempe = Temperatura
Serialprintln (tempe + t + Grados Centrigrados )
)
Algorithm 2 Section of the Arduino code
SerialPort conectar = new SerialPort (COM4 9600)
conectarOpen( )
while ( shouldStop)
if (conectarBytesToRead gt 0)
string t = conectarReadLine( )
string h = conectarReadLine( )
string m = conectarReadLine( )
string texto
t = tReplace(r )
resp textText = t
hume textText = h
mov textText = m
if (tStartWith(Temperatura ))
Texto = t
t = tReplace(Temperatura )
else resp textText =
if (hStartWith(Humedad ))
texto = h
h = hReplace(Humedad )
else hume textText =
Algorithm 3 Application connection
according to the graphic that shows the comparison betweenthe volume of water from the system and the manual ortraditional volume gives us a difference of 798 over the bestresult shown in Perez-Gutierrez et al [19] which shows thatthe system can efficiently manage water and that incorpora-tion of the expert knowledge in fuzzy rules allows irrigationscheme without using the pan evaporation or calculating thevolume associated with irrigation as they can be automat-ically supplied without the crop being adversely affected inits development Saving water is about 1524823m3 for a cropof 1000 plants in an 85-day cycle causing transplantation atthe 30th day The microclimate conditions generated by theinfrastructure of shadow house are not exploited by the lack
of modernization that is a producer who does not have theability to use technology to irrigate their crops generally onlyconsidered during the morning or afternoon which doesnot guarantee the product quality since plants could fall ineither water stress or excess moisture However the proposedsystem allows for irrigation supply data from three commonsensors (temperature relative humidity and soil moisture)allowing similarly for reducing infrastructure costs
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
12 International Journal of Distributed Sensor Networks
public void bd tempe( )
SqlConnection con = new SqlConnection()
conConnectionString = Data Source=DAVIDSERVERSQLEXPRESS
Initial Catalog=ARDUINOIntegrated Security=True
try
conOpen( )
catch (SqlException ex)
MessageBoxShow(exMessage)
throw
string tiempo = DateTimeNowToString( )
SqlCommand instruccion = conCreateCommand( )
instruccionCommandText = insert into tempe(horatemperatura)
The authors gratefully acknowledge the support of the Tech-nological Institute of Conkal ofMexico and Prometeo Projectof the Secretariat for Higher Education Science Technologyand Innovation of the Republic of Ecuador and CYTEDnetwork 514RT0486
References
[1] FAOSTAT FAO Statistical Databases amp Data-Sets Food andAgriculture Organization of the United Nations 2012 (Consul-tado Dic 2013) httpfaostatfaoorgsite567DesktopDefaultaspxPageID=567ancor
[2] SIAP Agri-Food and Fishing Information and Statistics ServiceSAGARPA Anuario estadıstico de la Produccion AgrıcolaMexico City Mexico 2011
[3] USDA and FAS ldquoGreenhouse and shade house production tocontinue increasingrdquo GAIN Report MX0024 vol 22 USDAFAS Mexico DF Mexico 2010
[4] H Turral M Svendsen and J M Faures ldquoInvesting in irriga-tion reviewing the past and looking to the futurerdquo AgriculturalWater Management vol 97 no 4 pp 551ndash560 2010
[5] S Tang Q Zhu X Zhou S Liu and M Wu ldquoA conceptionof digital agriculturerdquo in Proceedings of the IEEE InternationalGeoscience and Remote Sensing Symposium (IGARSS rsquo02) vol5 pp 3026ndash3028 June 2002
[6] L Bacci P Battista and B Rapi ldquoAn integrated method forirrigation scheduling of potted plantsrdquo Scientia Horticulturaevol 116 no 1 pp 89ndash97 2008
[7] R Lopez Lopez R Arteaga Ramırez M A Vazquez Pena ILopez Cruz and I Sanchez Cohen ldquoIndice de estres hıdricocomo un indicador del momento de riego en cultivos agrıcolasrdquoAgricultura Tecnica en Mexico vol 35 no 1 pp 97ndash111 2009
[8] N Livellara F Saavedra and E Salgado ldquoPlant based indicatorsfor irrigation scheduling in young cherry treesrdquo AgriculturalWater Management vol 98 no 4 pp 684ndash690 2011
[9] R Qiu S Kang F Li et al ldquoEnergy partitioning and evapotran-spiration of hot pepper grown in greenhouse with furrow anddrip irrigation methodsrdquo Scientia Horticulturae vol 129 no 4pp 790ndash797 2011
[10] J Casadesus M Mata J Marsal and J Girona ldquoA generalalgorithm for automated scheduling of drip irrigation in treecropsrdquo Computers and Electronics in Agriculture vol 83 pp 11ndash20 2012
[11] C O Akinbile and M S Yusoff ldquoGrowth yield and water usepattern of chilli pepper under different irrigation schedulingand managementrdquo Asian Journal of Agricultural Research vol5 no 2 pp 154ndash163 2011
[12] Y Huang Y Lan S J Thomson A Fang W C Hoffmann andR E Lacey ldquoDevelopment of soft computing and applicationsin agricultural and biological engineeringrdquo Computers andElectronics in Agriculture vol 71 no 2 pp 107ndash127 2010
[13] M Omid M Lashgari H Mobli R Alimardani S Mohtasebiand R Hesamifard ldquoDesign of fuzzy logic control systemincorporating human expert knowledge for combine harvesterrdquoExpert Systems with Applications vol 37 no 10 pp 7080ndash70852010
[14] A Merot and J-E Bergez ldquoIRRIGATE a dynamic integratedmodel combining a knowledge-based model and mechanisticbiophysical models for border irrigation managementrdquo Envi-ronmental Modelling and Software vol 25 no 4 pp 421ndash4322010
[15] S L Davis and M D Dukes ldquoIrrigation scheduling perfor-mance by evapotranspiration-based controllersrdquo AgriculturalWater Management vol 98 no 1 pp 19ndash28 2010
[16] Z Yao G Lou Z XiuLi and Q Zhao ldquoResearch and devel-opment precision irrigation control system in agriculturalrdquoin Proceedings of the International Conference on Computerand Communication Technologies in Agriculture Engineering(CCTAE rsquo10) pp 117ndash120 June 2010
[17] E Garcia Modificaciones al Sistema de Clasificacion Climaticode Koppen Serie de Libros no 6 UNAM Instituto deGeografıaMexico City Mexico 5th edition 2004
International Journal of Distributed Sensor Networks 13
[18] K M Passino S Yurkovich and M Reinfrank Fuzzy Controlvol 42 Addison Wesley Longman Menlo Park Calif USA1998
[19] A Perez-Gutierrez A Pineda-Doporto L Latournerie-Moreno and C Godoy-Avila ldquoNiveles de evapotranspiracionpotencial en la produccion de chile habanerordquo Terra Latino-americana vol 26 no 1 pp 53ndash59 2008
[20] W C Q Ortiz A Perez-Gutierrez L L Moreno C May-LaraE R Sanchez and A J M Chacon ldquoUso de agua potencialhıdrico y rendimiento de chile habanero (Capsicum chinenseJacq)rdquo Revista Fitotecnia Mexicana vol 35 no 2 pp 155ndash1602012
On the other hand fuzzy irrigation schema is proposedThe proposed system consists of four sections first measur-ing and estimating the volume of water lost subsequentlymeasuring level categorization through measuring the rela-tive humidity and temperature further the integration of thecommunication system and finally a fuzzy mechanism forthe estimation of irrigation
21 Measuring and Estimating the Volume of Water The soilused was characterized to obtain the parameters of Table 2 toidentify themoisture levels percentageWith these propertiesfield capacity is obtained which is the volume of water that
Table 2 Soil properties
Physical properties UnitApparent density 093 gsdotcmminus3
Wilting point 173Field capacity 558Chemical properties UnitPh 588CE 385 dSmminus1
is capable of retaining the soil of 14 MLCMminus2 Once knownfield capacity using traditional irrigation scheme a volumebetween 70 and 80 range is chosen to make growingHabanero pepper develop properly
The proposed system can be integrated into an algorithm(Figure 2 and Algorithm 1)
To measure the volume the VH400-2M sensor is usedThis element provides a signal from 0 to 3V in accordancewith soil moisture It can be seen that the useful limits of thesensor are 50 (Figure 3)
The interpolation of the measured data results in
119881 = 42times 10minus51198671198783 minus 000381198671198782 + 01265119867119878
+ 00888(1)
Equation (1) can be simplified for the range of interest inorder to reduce processing time as indicated in the sensordata sheet resulting in the linear relation (2)This equation isused to convert the voltage in a soil moisture value
119867119878 = 2632119881minus 789 (2)
In this way with this sensor the estimated lost soil moistureis obtained
22 Measurement of Relative Humidity and TemperatureThe measurement of relative humidity and temperature isperformed by a sensor DHT11This sensor is characterized bythe calibrated digital signal It consists of two resistive sensors
4 International Journal of Distributed Sensor Networks
Table 3 Rules of the fuzzy system in the growth stage of the crop
TemperatureVery few Few Medium High Very high
HumidityVery few Medium Medium Long Long Very longFew Medium Medium Medium Medium LongMedium Short Short Short Short MediumHigh Very short Very short Very short Very short Very shortVery high Very short Very short Very short Very short Very short
Start
Measurement and estimation ofthe volume of water lost
Yes Is it in theoptimal range
No
NoThe volume isbelow 70
Yes
Valuation of thevolume trend
Irrigation isscheduled
No
Yes
Fuzzy mechanism forestimating irrigation time
Irrigationrun
Yes Irrigationended No
Figure 2 Algorithm that incorporated the fuzzy irrigation process
(NTC and humidity) and a small 8-bit microcontroller It canmeasure the humidity in the range 20 approximately 95and the temperature range between 0∘C and 50∘C Protocoluses 1-wire communication and the size is small and has lowpower consumption and the ability to transmit the signal upto 20 meters away
23 Fuzzy System
231 Fuzzy Rules A model for the climate behavior is toocomplex and the uncertainty is always present so the use ofthe fuzzy system in order to program an irrigation schemeis proposed The fuzzy system uses the expert knowledge inform of rules to control the aperture time of the valve andtherefor the water supplied The knowledge base from the
expert provides the information necessary to perform thedesign of the irrigation program sequence as well as thegeneration of the rules that are part of the fuzzy inferencemechanism (see Table 3)
The proposed model employs Mamdani fuzzy mecha-nism described with MISO type fuzzy rules shown on thesystem equation
1198771 If 1199061 is 1198601198951 1199062 is 1198601198962 119906119899 is 119860
119897
119899then 1199101 is 1198611199031
119877119895 If 1199061 is 1198601198951 1199062 is 1198601198962 119906119899 is 119860
119897
119899then 119910
119895is 119861119903119895
119877119898 If 1199061 is 1198601198951 1199062 is 1198601198962 119906119899 is 119860
119897
119899then 119910
119898is 119861119905119898
(3)
International Journal of Distributed Sensor Networks 5
35
30
25
20
15
10
05
0100 20 30
Out
put v
olta
ge (V
)
40 50 60
Soil moisture ()
y = 42E minus 5x3 minus 00038x2 + 01265x + 00888R2 = 09974
Figure 3 Humidity sensor characterization
where 119860119895119894 119860119896
2 119860
119897
119899and 119861119904
119895for 119877119895 represent the corre-
sponding linguistic values [18]The processing block consistsof a hardware Arduino microcontroller which is the Atmel328p where the irrigation program established by the fuzzyrules is stored
232 Fuzzy Sets The linguistic variables to form the fuzzysets are as follows
(i) Temperature Due to the warm air in the shadow houseinfrastructure is retained and an important consideration inthe cultivation under protected conditions is the temperaturefactor because it favors the evaporation of water and has animportant impact in the crop The low technification of thisstructure only can provide air movement through the roofdoors or the antiaphid mesh around of him
(ii) RelativeHumidityThe humidity factor has great influenceon the crop Excess moisture in habanero pepper plantsaffects its development so it should not be given water to thecrop when the humidity is high however when the humiditydecreases it is necessary to supply water to the plant
(iii) Stages of the Crop Growth stages (119870119888) influence the devel-
opment of the plant especially considering that the habaneropepper is a plant that requires large amounts of water whichultimately affects the quality of the fruit This is an importantfactor for the habanero pepper because to obtain designationof origin you must have certain characteristics of the fruit atharvest
(iv) Irrigation Time The output of the fuzzy system is theirrigation time and represents the run time in minutes Thefrequency of watering time keeps the amount of water neededto avoid crop water stress
In order to convert the linguistic variables on fuzzynumbers this paper proposes the calculation of intermediatevalues of the linguistic range through the triangular function(4) while outliers are modeled by the Gamma function left
10
30 53 75 98 120
05
Growth
MaturityDevelopment
MaxMin
Figure 4 Input sets for the variable stage of development
part of the linguistic rang (5) and 119871 function right part of thelinguistic range (6)
120583 (119909 119886119898 119887)
= max min (119909 minus 119886) (119898 minus 119886) minus 1 (119887 minus 119909) (119887 minus 119898) minus 1 0 (4)
120583 (119909) =
0 119909 le 119886
(119909 minus 119886) (119898 minus 119886)minus1119909 isin (119886119898)
1 119909 ge 119898
(5)
The membership function is created with expert knowledgeand adopts a graphic form determined according to the typeof value associated with the fuzzy set The 119871 and Gammafunctions correspond to the membership functions whichare used to calculate extreme fuzzy values Therefore the 119871function is defined by
119871 = 1minusGamma (6)
The graphic form of the linguistic variables defined by thelast equations is shown in Figures 4 5 6 and 7
233 Inference Mechanism The inference mechanism hastwo basic tasks determines the relevance and extent of eachrule in relation to the current entries (119906
119894) and generates the
corresponding conclusions The combination of the sets ofrules with inputs can be calculated as follows
6 International Journal of Distributed Sensor Networks
10
05
130 175 220 265 310 355 400
MaxMin
Medium
Very fewFew Very high
High
Figure 5 Input sets for the variable relative humidity
10
05
300 4166 7666 8833 10005333 650
MaxMin
Medium
Very fewFew Very high
High
Figure 6 Input sets for the variable temperature
The result is a set with the ldquofiredrdquo rules To obtain a crispvalue that can be applied to the valve it will be necessary tocalculate the center of the graph and this can be done by
119910 =
int119910120583 (119910) 119910 119889119910
int119910120583 (119910) 119910 119889119910
(8)
24 Data Acquisition System The proposed communicationsystem includes a basic structure that can be used in agricul-tural environments and this proposal aims to organize thesections of a sensor network and facilitates the selection oftechnologies required to implement the ZigBee networkThecommunication system uses IEEE802154 protocol (ZigBee)which was implemented on an Arduino board with XBee Promodule of Maxtream configured with a PAN ID 3332 a rateof 9600 baud 8 data bits and no parity (see Figure 8)
The structure presented is divided into two sections aninternal for data collection (sensor network) and an exter-nal that can send information to central computers to storeandor process informationThe internal section is composedof elements that collect information from the agriculturalparameters of interest such as temperature relative humidityand soil moisture (sensors) the data collected will be sent todevices for processing through the ZigBee protocol which
10
05
0 333 667 100 1333 1667 200
Medium
Very shortShort Very long
Long
MaxMin
Figure 7 Output sets for the variable irrigation time
Network PAN id = 3332
CoordinatorDH 0DL FFFFMY 1
Fuzzy segmentDH 0DL 1MY A
Traditional segmentDH 0DL 1MY B
Figure 8 Sensor network
is used for transmission (end devices) The internal sectioncan be implemented through Arduino boards with Xbeemodules In the transmission of information the router nodewill receive the information of the end devices and will betransmitted to the coordinator for central processing in orderto be able to connect to a larger networkThe external sectionis composed of central processing devices sending data toremote nodes via Ethernet WiFi mobile devices or othermeans that can send information to other locations as shownin Figure 9
The data acquisition is made with a DuemilanovaArduino and this platform is programed for a sampling rateof 5 minutes Each value from the corresponding sensor hasan header for identification as shown in Algorithm 2 Thesedata are linked with the Xbee devices for the transmission tothe other Xbee configured like a coordinator
To get data from the serial port that the Xbee Shield(Coordinator) is sending we programmethods as can be seenin Algorithm 3 and the header of each data frame is definedso that it recognizes and takes the indicated action for storageat the database every time that receives data (Algorithm 4)
3 Results and Discussion
The behavior of the proposed system was supervised fromMarch to May 2013 this corresponds to all cycle of cropTables 4(a) 4(b) and 4(c) show the average values acquiredfrom the system The results obtained are compared with
International Journal of Distributed Sensor Networks 7
Shaded houseinfrastructure
Sensors
Sensors
SensorsSensors
SensorsSensors
External section
ZigBee
ZigBeeZigBee
coordinator
end device
ZigBeeend device
Internal section
router
GPRS Ethernet WiFi
Centralprocessing
Arduino + Xbee
Arduino + Xbee
50m
Figure 9 Internal and external sections
65432100 10 20 30 40 50 60 70
Days after transplant
Etc (
mm
)
Figure 10 Evapotranspiration values for the crop cycle
the results of calculations performed for the water loss byevapotranspiration reference (Eto) as shown in the work ofPerez-Gutierrez et al [19] The comparison between fuzzysystems of irrigation and traditional irrigationwas performedto validate the data obtained
The Etc shows values that are in an interval of 3mmsdotdayminus1(Figure 10) This range is stable for all the harvest period ifvariations of the Etc were more spacious and then shouldtake into account a greater number of variables thereforeby maintaining a controlled environment with the use of theshadow house we can use a small number of sensors and inthis proposal the use of three of them is enough to estimatethe irrigation
The system showed a deviation of +011 in Marchminus039 in April and +051 in May (Figures 11 12 and13) The supplied volume by the fuzzy system is comparedwith the calculated volume resulting in a small deviationtherefore we can establish the reliability of the system
0500
10001500200025003000350040004500
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
Volu
me (
L)
Days (March)
Calculated volume (L)Supplied volume (L)
Figure 11 Comparison between traditional irrigation and the fuzzysystem (March 2013)
With the fuzzy system validated the analysis of the datafor eachmonth can be made As part of the validation sampleMarch 20 is shown In Figure 14 the frequency of irrigationduring the day is graphed
According to the data obtained with respect to thefrequency of irrigation Figure 14 shows that the frequencyof irrigation is higher in the time period between 1030and 1300 in this period we can observe an increase trendin the temperature (Figure 15) and a low relative humidity(Figure 16) indicating a water accumulation in the soiltherefore a balance between evapotranspiration and thewater applied to the crop is generated
8 International Journal of Distributed Sensor Networks
Table4(a)D
atafrom
them
onth
ofMarch
forthe
cycle
ofcrop(b)
Datafrom
them
onth
ofAp
rilforthe
cycle
ofcrop(c)Datafrom
them
onth
ofMay
forthe
cycle
ofcrop
(a)
March
Day
Etc(mmsdotdıaminus1 )
FuzzyIrrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
Day
Etc
(mmsdotdıaminus1)
Fuzzy
Irrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy
(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
2258425473
1341340
92235568182
2029668918
17357783512
168119
182801986364
2810
025131
3257725364
10913627
1818
937879
2024170272
18307273124
14404782
240079697
2413
317471
437044
9566
15902236
2650372727
2909504
086
19373218858
17796109
2966018182
2931254056
5384566297
182036
3033933333
3020376637
2039600
9363
18436127
3072
687879
3110
250267
6333154632
149705
2495
083333
2616
59036
2134154618
15833791
2638965152
2682497426
7307303393
14569518
2472
201515
2413
555204
22444
59937
22096245
3682707576
3491
875286
837064
8946
17890227
2981704545
2911070015
23450743937
2116
9736
3528289394
3540134599
93627264
2817962364
2993
727273
2848846
704
24467591342
21811636
3635272727
3672
453809
10383003911
1852
4945
3174
921212
3008105683
2513
804632
566
89909
9448318182
1084213265
112655606
8411413364
1902227273
2085708733
26096752606
45595818
759930303
7598931903
1224746
8837
11749273
1999
775758
1943615697
27366104898
17867809
2977
968182
287538114
113
231084615
1112
2518
185375303
1814
934321
28346
753609
1576
0455
2626742424
2723396
476
14242255034
10806
673
1801112121
1902666591
29460879854
22293755
3715
625758
3619
741905
15349001336
1674
8164
2791
3606
062741050084
30462318448
21561836
3593
639394
3631040
596
16369959684
17351655
2891
942424
2905656565
31488327963
22941936
3823656061
3835318856
(b)
April
Day
Etc(mmsdotdıaminus1)
FuzzyIrrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
Day
Etc
(mmsdotdıaminus1)
FuzzyIrrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
13961718653
18771482
3128580303
3111526554
16399938767
17371336
2895
222727
31411117
32
4169559209
25227564
4204593939
3274
764145
17409686903
192428
3207133333
3217
673412
3404
8530602
18909436
3151572727
3179
708499
18471907325
21807836
3634639394
370635146
44
3418113126
15290055
2548342424
2684579772
195057651274
22931773
3821962121
3972
270022
54344343997
20729182
3454863636
3412
039796
203687610895
14040
036
23400
06061
2896
242824
652164
59781
24479782
4079
963636
4096
997931
214583233577
21846
064
364101060
63599
663234
75021289634
23754855
3959142424
3943711656
223708343758
17238536
2873
089394
2912
526377
84818401971
25410745
4235124242
3784364
058
235197175777
24660
44110
0666
6740
8185231
94929600332
2337
8527
3896
421212
3871699047
245013674745
23637727
3939621212
3937730937
104425304
818
2076
9664
346161060
63475
626276
25460
4622494
2139
2809
3565468182
3616
46205
1143046
8744
720112073
3352012121
3380893615
264715697984
22946236
3824372727
3703700535
12303026964
214473727
2412
287879
2379
968211
274767017231
2235
9845
37266
40909
37440
06578
International Journal of Distributed Sensor Networks 9
(b)Con
tinued
April
Day
Etc(mmsdotdıaminus1)
FuzzyIrrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
Day
Etc
(mmsdotdıaminus1)
FuzzyIrrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
134228506725
1981396
43302327273
3321061416
2849999503
2313
4864
385581060
63926951783
14419946964
121049973
3508328788
3298
255743
294685183652
2216
5509
3694
251515
3679
734635
1541340
0889
18688127
3114
687879
3246842989
303708190798
17870818
2978
469697
2912
406242
(c)
May
Day
Etc(mmsdotdıaminus1)
FuzzyIrrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
Day
Etc
(mmsdotdıaminus1)
Fuzzy
Irrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy
(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
134840
0241
16046282
2674
3803
273632909
14281323798
12056146
200935758
22095119
42
428945109
19954027
332567121
336892701
15345795001
15992027
266533788
2715
86759
3364
868417
17514855
2919
14242
286566985
16376349121
17962409
2993
73485
295583908
4400339841
190312
317186667
314426176
17341760316
1610
1982
268366364
268417925
527064
0388
1241364
62068940
912125604
6418
343767282
15971582
26619303
2699
94192
6351451778
16295346
2715
89091
276029581
19313612887
15119536
2519
92273
243822456
7329792176
1609100
9268183485
2590
1817
20293326251
14367546
2394
59091
246310986
8335111737
15496273
258271212
263196142
21305215788
13911227
2318
53788
230377899
9358129742
16387591
273126515
2812
7444
222
304
887854
14010273
233504545
2397
15919
10365825019
1766
4609
294410152
287318298
23252884747
1503564
62505940
912394
58361
11377619727
17437236
2906206
0629658184
24252884747
113733
1895
55198615216
12373897867
17908982
29848303
293658698
25320855932
1554804
62591
34091
2519
99659
13283378267
1312
6791
218779848
222564771
10 International Journal of Distributed Sensor Networks
Calculated volume (L)Supplied volume (L)
0500
100015002000250030003500400045005000
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Volu
me (
L)
Days (April)
Figure 12 Comparison between traditional irrigation and the fuzzysystem (April 2013)
Calculated volume (L)Supplied volume (L)
0500
1000150020002500300035004000
1 3 5 7 9 11 13 15 17 19 21 23 25
Volu
me (
L)
Days (May)
Figure 13 Comparison between traditional irrigation and the fuzzysystem (May 2013)
These results establish the reliability of the proposedsystem to provide adequate water and maintain optimummoisture level with low relative humidity to obtain thedesirable quality of hbanero pepper production
In thework ofOrtiz et al [20] the percentage of optimumsoil moisture for growing good quality fruits is 60 the fuzzysystem employs the percentage of 70 indicated by the expertso that it is possible to also set the reliability in the finalquality of the fruit The work presented by Perez-Gutierrezet al [19] mentioned that the volume of water applied to80 of potential evapotranspiration generates the amount ofwater in the soil to favor a constant process of transpirationfruit yield and improvement of water use this requires awater volume 2223m3 haminus1 however the fuzzy system onlyrequires 44995m3 haminus1 which represents a saving of 798water
The proposed system is a better form to schedule irriga-tion unlike Yao et al [16] the objective is to give to the poorcommunities a form to harvest a high demand product likehabanero pepper (with designation of origin) with enoughtechnologic structure The work of Yao et al [16] is using aneural network to refine the sprinkle time based on a patternthat is not described in the paper in a real environmentespecially in Yucatan Mexico the humidity and temperaturevariables have to be considered to obtain a quality product
02468
101214161820
000 224 448 712 936 1200 1424 1648 1912
Dur
atio
n of
irrig
atio
n (m
in)
Hours of day
Figure 14 Irrigation frequency using the fuzzy system (sample of20 March 2013)
40
35
30
25
20
15
10
5
0600 824 1048 1312 1536 1800
Hours of day
Tem
pera
ture
(∘C)
Figure 15 Temperature measurement (sample of 20 March 2013)
1009080706050403020100600 824 1048
Hours of day1312 1536 1800
Tem
pera
ture
(∘C)
Figure 16 Measurement of relative humidity (sample of 20 March2013)
and unlike the work of Yao et al [16] in this work are takeninto account in the fuzzy model and in the expert knowledgeThe use of a neural network is not a significant contributionfor managing the time of irrigation on a shadow housebecause there are no other important variables included andin the Yao et al [16] proposal only the soil moisture and theerror are considered to set the irrigation time
4 Conclusions
This paper has presented a fuzzy system that manages awireless irrigation scheme through an algorithm that takesinto account the conditions of microclimate of a shadowhouse used for growing habanero pepper with designation oforigin in the state of Yucatan The water volume consumed
International Journal of Distributed Sensor Networks 11
float h = dhtreadHumidity( )
float t = dhtreadTemperature()
revisa si retorna un valor valido de lo contrario hay un error
if (isnan (t) || isnan (h)) (
Serialprintln (Failed to read from DHT)
) else (
String hume = Humedad
Serialprintln (hume + h + de Humedad)
String tempe = Temperatura
Serialprintln (tempe + t + Grados Centrigrados )
)
Algorithm 2 Section of the Arduino code
SerialPort conectar = new SerialPort (COM4 9600)
conectarOpen( )
while ( shouldStop)
if (conectarBytesToRead gt 0)
string t = conectarReadLine( )
string h = conectarReadLine( )
string m = conectarReadLine( )
string texto
t = tReplace(r )
resp textText = t
hume textText = h
mov textText = m
if (tStartWith(Temperatura ))
Texto = t
t = tReplace(Temperatura )
else resp textText =
if (hStartWith(Humedad ))
texto = h
h = hReplace(Humedad )
else hume textText =
Algorithm 3 Application connection
according to the graphic that shows the comparison betweenthe volume of water from the system and the manual ortraditional volume gives us a difference of 798 over the bestresult shown in Perez-Gutierrez et al [19] which shows thatthe system can efficiently manage water and that incorpora-tion of the expert knowledge in fuzzy rules allows irrigationscheme without using the pan evaporation or calculating thevolume associated with irrigation as they can be automat-ically supplied without the crop being adversely affected inits development Saving water is about 1524823m3 for a cropof 1000 plants in an 85-day cycle causing transplantation atthe 30th day The microclimate conditions generated by theinfrastructure of shadow house are not exploited by the lack
of modernization that is a producer who does not have theability to use technology to irrigate their crops generally onlyconsidered during the morning or afternoon which doesnot guarantee the product quality since plants could fall ineither water stress or excess moisture However the proposedsystem allows for irrigation supply data from three commonsensors (temperature relative humidity and soil moisture)allowing similarly for reducing infrastructure costs
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
12 International Journal of Distributed Sensor Networks
public void bd tempe( )
SqlConnection con = new SqlConnection()
conConnectionString = Data Source=DAVIDSERVERSQLEXPRESS
Initial Catalog=ARDUINOIntegrated Security=True
try
conOpen( )
catch (SqlException ex)
MessageBoxShow(exMessage)
throw
string tiempo = DateTimeNowToString( )
SqlCommand instruccion = conCreateCommand( )
instruccionCommandText = insert into tempe(horatemperatura)
The authors gratefully acknowledge the support of the Tech-nological Institute of Conkal ofMexico and Prometeo Projectof the Secretariat for Higher Education Science Technologyand Innovation of the Republic of Ecuador and CYTEDnetwork 514RT0486
References
[1] FAOSTAT FAO Statistical Databases amp Data-Sets Food andAgriculture Organization of the United Nations 2012 (Consul-tado Dic 2013) httpfaostatfaoorgsite567DesktopDefaultaspxPageID=567ancor
[2] SIAP Agri-Food and Fishing Information and Statistics ServiceSAGARPA Anuario estadıstico de la Produccion AgrıcolaMexico City Mexico 2011
[3] USDA and FAS ldquoGreenhouse and shade house production tocontinue increasingrdquo GAIN Report MX0024 vol 22 USDAFAS Mexico DF Mexico 2010
[4] H Turral M Svendsen and J M Faures ldquoInvesting in irriga-tion reviewing the past and looking to the futurerdquo AgriculturalWater Management vol 97 no 4 pp 551ndash560 2010
[5] S Tang Q Zhu X Zhou S Liu and M Wu ldquoA conceptionof digital agriculturerdquo in Proceedings of the IEEE InternationalGeoscience and Remote Sensing Symposium (IGARSS rsquo02) vol5 pp 3026ndash3028 June 2002
[6] L Bacci P Battista and B Rapi ldquoAn integrated method forirrigation scheduling of potted plantsrdquo Scientia Horticulturaevol 116 no 1 pp 89ndash97 2008
[7] R Lopez Lopez R Arteaga Ramırez M A Vazquez Pena ILopez Cruz and I Sanchez Cohen ldquoIndice de estres hıdricocomo un indicador del momento de riego en cultivos agrıcolasrdquoAgricultura Tecnica en Mexico vol 35 no 1 pp 97ndash111 2009
[8] N Livellara F Saavedra and E Salgado ldquoPlant based indicatorsfor irrigation scheduling in young cherry treesrdquo AgriculturalWater Management vol 98 no 4 pp 684ndash690 2011
[9] R Qiu S Kang F Li et al ldquoEnergy partitioning and evapotran-spiration of hot pepper grown in greenhouse with furrow anddrip irrigation methodsrdquo Scientia Horticulturae vol 129 no 4pp 790ndash797 2011
[10] J Casadesus M Mata J Marsal and J Girona ldquoA generalalgorithm for automated scheduling of drip irrigation in treecropsrdquo Computers and Electronics in Agriculture vol 83 pp 11ndash20 2012
[11] C O Akinbile and M S Yusoff ldquoGrowth yield and water usepattern of chilli pepper under different irrigation schedulingand managementrdquo Asian Journal of Agricultural Research vol5 no 2 pp 154ndash163 2011
[12] Y Huang Y Lan S J Thomson A Fang W C Hoffmann andR E Lacey ldquoDevelopment of soft computing and applicationsin agricultural and biological engineeringrdquo Computers andElectronics in Agriculture vol 71 no 2 pp 107ndash127 2010
[13] M Omid M Lashgari H Mobli R Alimardani S Mohtasebiand R Hesamifard ldquoDesign of fuzzy logic control systemincorporating human expert knowledge for combine harvesterrdquoExpert Systems with Applications vol 37 no 10 pp 7080ndash70852010
[14] A Merot and J-E Bergez ldquoIRRIGATE a dynamic integratedmodel combining a knowledge-based model and mechanisticbiophysical models for border irrigation managementrdquo Envi-ronmental Modelling and Software vol 25 no 4 pp 421ndash4322010
[15] S L Davis and M D Dukes ldquoIrrigation scheduling perfor-mance by evapotranspiration-based controllersrdquo AgriculturalWater Management vol 98 no 1 pp 19ndash28 2010
[16] Z Yao G Lou Z XiuLi and Q Zhao ldquoResearch and devel-opment precision irrigation control system in agriculturalrdquoin Proceedings of the International Conference on Computerand Communication Technologies in Agriculture Engineering(CCTAE rsquo10) pp 117ndash120 June 2010
[17] E Garcia Modificaciones al Sistema de Clasificacion Climaticode Koppen Serie de Libros no 6 UNAM Instituto deGeografıaMexico City Mexico 5th edition 2004
International Journal of Distributed Sensor Networks 13
[18] K M Passino S Yurkovich and M Reinfrank Fuzzy Controlvol 42 Addison Wesley Longman Menlo Park Calif USA1998
[19] A Perez-Gutierrez A Pineda-Doporto L Latournerie-Moreno and C Godoy-Avila ldquoNiveles de evapotranspiracionpotencial en la produccion de chile habanerordquo Terra Latino-americana vol 26 no 1 pp 53ndash59 2008
[20] W C Q Ortiz A Perez-Gutierrez L L Moreno C May-LaraE R Sanchez and A J M Chacon ldquoUso de agua potencialhıdrico y rendimiento de chile habanero (Capsicum chinenseJacq)rdquo Revista Fitotecnia Mexicana vol 35 no 2 pp 155ndash1602012
4 International Journal of Distributed Sensor Networks
Table 3 Rules of the fuzzy system in the growth stage of the crop
TemperatureVery few Few Medium High Very high
HumidityVery few Medium Medium Long Long Very longFew Medium Medium Medium Medium LongMedium Short Short Short Short MediumHigh Very short Very short Very short Very short Very shortVery high Very short Very short Very short Very short Very short
Start
Measurement and estimation ofthe volume of water lost
Yes Is it in theoptimal range
No
NoThe volume isbelow 70
Yes
Valuation of thevolume trend
Irrigation isscheduled
No
Yes
Fuzzy mechanism forestimating irrigation time
Irrigationrun
Yes Irrigationended No
Figure 2 Algorithm that incorporated the fuzzy irrigation process
(NTC and humidity) and a small 8-bit microcontroller It canmeasure the humidity in the range 20 approximately 95and the temperature range between 0∘C and 50∘C Protocoluses 1-wire communication and the size is small and has lowpower consumption and the ability to transmit the signal upto 20 meters away
23 Fuzzy System
231 Fuzzy Rules A model for the climate behavior is toocomplex and the uncertainty is always present so the use ofthe fuzzy system in order to program an irrigation schemeis proposed The fuzzy system uses the expert knowledge inform of rules to control the aperture time of the valve andtherefor the water supplied The knowledge base from the
expert provides the information necessary to perform thedesign of the irrigation program sequence as well as thegeneration of the rules that are part of the fuzzy inferencemechanism (see Table 3)
The proposed model employs Mamdani fuzzy mecha-nism described with MISO type fuzzy rules shown on thesystem equation
1198771 If 1199061 is 1198601198951 1199062 is 1198601198962 119906119899 is 119860
119897
119899then 1199101 is 1198611199031
119877119895 If 1199061 is 1198601198951 1199062 is 1198601198962 119906119899 is 119860
119897
119899then 119910
119895is 119861119903119895
119877119898 If 1199061 is 1198601198951 1199062 is 1198601198962 119906119899 is 119860
119897
119899then 119910
119898is 119861119905119898
(3)
International Journal of Distributed Sensor Networks 5
35
30
25
20
15
10
05
0100 20 30
Out
put v
olta
ge (V
)
40 50 60
Soil moisture ()
y = 42E minus 5x3 minus 00038x2 + 01265x + 00888R2 = 09974
Figure 3 Humidity sensor characterization
where 119860119895119894 119860119896
2 119860
119897
119899and 119861119904
119895for 119877119895 represent the corre-
sponding linguistic values [18]The processing block consistsof a hardware Arduino microcontroller which is the Atmel328p where the irrigation program established by the fuzzyrules is stored
232 Fuzzy Sets The linguistic variables to form the fuzzysets are as follows
(i) Temperature Due to the warm air in the shadow houseinfrastructure is retained and an important consideration inthe cultivation under protected conditions is the temperaturefactor because it favors the evaporation of water and has animportant impact in the crop The low technification of thisstructure only can provide air movement through the roofdoors or the antiaphid mesh around of him
(ii) RelativeHumidityThe humidity factor has great influenceon the crop Excess moisture in habanero pepper plantsaffects its development so it should not be given water to thecrop when the humidity is high however when the humiditydecreases it is necessary to supply water to the plant
(iii) Stages of the Crop Growth stages (119870119888) influence the devel-
opment of the plant especially considering that the habaneropepper is a plant that requires large amounts of water whichultimately affects the quality of the fruit This is an importantfactor for the habanero pepper because to obtain designationof origin you must have certain characteristics of the fruit atharvest
(iv) Irrigation Time The output of the fuzzy system is theirrigation time and represents the run time in minutes Thefrequency of watering time keeps the amount of water neededto avoid crop water stress
In order to convert the linguistic variables on fuzzynumbers this paper proposes the calculation of intermediatevalues of the linguistic range through the triangular function(4) while outliers are modeled by the Gamma function left
10
30 53 75 98 120
05
Growth
MaturityDevelopment
MaxMin
Figure 4 Input sets for the variable stage of development
part of the linguistic rang (5) and 119871 function right part of thelinguistic range (6)
120583 (119909 119886119898 119887)
= max min (119909 minus 119886) (119898 minus 119886) minus 1 (119887 minus 119909) (119887 minus 119898) minus 1 0 (4)
120583 (119909) =
0 119909 le 119886
(119909 minus 119886) (119898 minus 119886)minus1119909 isin (119886119898)
1 119909 ge 119898
(5)
The membership function is created with expert knowledgeand adopts a graphic form determined according to the typeof value associated with the fuzzy set The 119871 and Gammafunctions correspond to the membership functions whichare used to calculate extreme fuzzy values Therefore the 119871function is defined by
119871 = 1minusGamma (6)
The graphic form of the linguistic variables defined by thelast equations is shown in Figures 4 5 6 and 7
233 Inference Mechanism The inference mechanism hastwo basic tasks determines the relevance and extent of eachrule in relation to the current entries (119906
119894) and generates the
corresponding conclusions The combination of the sets ofrules with inputs can be calculated as follows
6 International Journal of Distributed Sensor Networks
10
05
130 175 220 265 310 355 400
MaxMin
Medium
Very fewFew Very high
High
Figure 5 Input sets for the variable relative humidity
10
05
300 4166 7666 8833 10005333 650
MaxMin
Medium
Very fewFew Very high
High
Figure 6 Input sets for the variable temperature
The result is a set with the ldquofiredrdquo rules To obtain a crispvalue that can be applied to the valve it will be necessary tocalculate the center of the graph and this can be done by
119910 =
int119910120583 (119910) 119910 119889119910
int119910120583 (119910) 119910 119889119910
(8)
24 Data Acquisition System The proposed communicationsystem includes a basic structure that can be used in agricul-tural environments and this proposal aims to organize thesections of a sensor network and facilitates the selection oftechnologies required to implement the ZigBee networkThecommunication system uses IEEE802154 protocol (ZigBee)which was implemented on an Arduino board with XBee Promodule of Maxtream configured with a PAN ID 3332 a rateof 9600 baud 8 data bits and no parity (see Figure 8)
The structure presented is divided into two sections aninternal for data collection (sensor network) and an exter-nal that can send information to central computers to storeandor process informationThe internal section is composedof elements that collect information from the agriculturalparameters of interest such as temperature relative humidityand soil moisture (sensors) the data collected will be sent todevices for processing through the ZigBee protocol which
10
05
0 333 667 100 1333 1667 200
Medium
Very shortShort Very long
Long
MaxMin
Figure 7 Output sets for the variable irrigation time
Network PAN id = 3332
CoordinatorDH 0DL FFFFMY 1
Fuzzy segmentDH 0DL 1MY A
Traditional segmentDH 0DL 1MY B
Figure 8 Sensor network
is used for transmission (end devices) The internal sectioncan be implemented through Arduino boards with Xbeemodules In the transmission of information the router nodewill receive the information of the end devices and will betransmitted to the coordinator for central processing in orderto be able to connect to a larger networkThe external sectionis composed of central processing devices sending data toremote nodes via Ethernet WiFi mobile devices or othermeans that can send information to other locations as shownin Figure 9
The data acquisition is made with a DuemilanovaArduino and this platform is programed for a sampling rateof 5 minutes Each value from the corresponding sensor hasan header for identification as shown in Algorithm 2 Thesedata are linked with the Xbee devices for the transmission tothe other Xbee configured like a coordinator
To get data from the serial port that the Xbee Shield(Coordinator) is sending we programmethods as can be seenin Algorithm 3 and the header of each data frame is definedso that it recognizes and takes the indicated action for storageat the database every time that receives data (Algorithm 4)
3 Results and Discussion
The behavior of the proposed system was supervised fromMarch to May 2013 this corresponds to all cycle of cropTables 4(a) 4(b) and 4(c) show the average values acquiredfrom the system The results obtained are compared with
International Journal of Distributed Sensor Networks 7
Shaded houseinfrastructure
Sensors
Sensors
SensorsSensors
SensorsSensors
External section
ZigBee
ZigBeeZigBee
coordinator
end device
ZigBeeend device
Internal section
router
GPRS Ethernet WiFi
Centralprocessing
Arduino + Xbee
Arduino + Xbee
50m
Figure 9 Internal and external sections
65432100 10 20 30 40 50 60 70
Days after transplant
Etc (
mm
)
Figure 10 Evapotranspiration values for the crop cycle
the results of calculations performed for the water loss byevapotranspiration reference (Eto) as shown in the work ofPerez-Gutierrez et al [19] The comparison between fuzzysystems of irrigation and traditional irrigationwas performedto validate the data obtained
The Etc shows values that are in an interval of 3mmsdotdayminus1(Figure 10) This range is stable for all the harvest period ifvariations of the Etc were more spacious and then shouldtake into account a greater number of variables thereforeby maintaining a controlled environment with the use of theshadow house we can use a small number of sensors and inthis proposal the use of three of them is enough to estimatethe irrigation
The system showed a deviation of +011 in Marchminus039 in April and +051 in May (Figures 11 12 and13) The supplied volume by the fuzzy system is comparedwith the calculated volume resulting in a small deviationtherefore we can establish the reliability of the system
0500
10001500200025003000350040004500
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
Volu
me (
L)
Days (March)
Calculated volume (L)Supplied volume (L)
Figure 11 Comparison between traditional irrigation and the fuzzysystem (March 2013)
With the fuzzy system validated the analysis of the datafor eachmonth can be made As part of the validation sampleMarch 20 is shown In Figure 14 the frequency of irrigationduring the day is graphed
According to the data obtained with respect to thefrequency of irrigation Figure 14 shows that the frequencyof irrigation is higher in the time period between 1030and 1300 in this period we can observe an increase trendin the temperature (Figure 15) and a low relative humidity(Figure 16) indicating a water accumulation in the soiltherefore a balance between evapotranspiration and thewater applied to the crop is generated
8 International Journal of Distributed Sensor Networks
Table4(a)D
atafrom
them
onth
ofMarch
forthe
cycle
ofcrop(b)
Datafrom
them
onth
ofAp
rilforthe
cycle
ofcrop(c)Datafrom
them
onth
ofMay
forthe
cycle
ofcrop
(a)
March
Day
Etc(mmsdotdıaminus1 )
FuzzyIrrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
Day
Etc
(mmsdotdıaminus1)
Fuzzy
Irrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy
(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
2258425473
1341340
92235568182
2029668918
17357783512
168119
182801986364
2810
025131
3257725364
10913627
1818
937879
2024170272
18307273124
14404782
240079697
2413
317471
437044
9566
15902236
2650372727
2909504
086
19373218858
17796109
2966018182
2931254056
5384566297
182036
3033933333
3020376637
2039600
9363
18436127
3072
687879
3110
250267
6333154632
149705
2495
083333
2616
59036
2134154618
15833791
2638965152
2682497426
7307303393
14569518
2472
201515
2413
555204
22444
59937
22096245
3682707576
3491
875286
837064
8946
17890227
2981704545
2911070015
23450743937
2116
9736
3528289394
3540134599
93627264
2817962364
2993
727273
2848846
704
24467591342
21811636
3635272727
3672
453809
10383003911
1852
4945
3174
921212
3008105683
2513
804632
566
89909
9448318182
1084213265
112655606
8411413364
1902227273
2085708733
26096752606
45595818
759930303
7598931903
1224746
8837
11749273
1999
775758
1943615697
27366104898
17867809
2977
968182
287538114
113
231084615
1112
2518
185375303
1814
934321
28346
753609
1576
0455
2626742424
2723396
476
14242255034
10806
673
1801112121
1902666591
29460879854
22293755
3715
625758
3619
741905
15349001336
1674
8164
2791
3606
062741050084
30462318448
21561836
3593
639394
3631040
596
16369959684
17351655
2891
942424
2905656565
31488327963
22941936
3823656061
3835318856
(b)
April
Day
Etc(mmsdotdıaminus1)
FuzzyIrrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
Day
Etc
(mmsdotdıaminus1)
FuzzyIrrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
13961718653
18771482
3128580303
3111526554
16399938767
17371336
2895
222727
31411117
32
4169559209
25227564
4204593939
3274
764145
17409686903
192428
3207133333
3217
673412
3404
8530602
18909436
3151572727
3179
708499
18471907325
21807836
3634639394
370635146
44
3418113126
15290055
2548342424
2684579772
195057651274
22931773
3821962121
3972
270022
54344343997
20729182
3454863636
3412
039796
203687610895
14040
036
23400
06061
2896
242824
652164
59781
24479782
4079
963636
4096
997931
214583233577
21846
064
364101060
63599
663234
75021289634
23754855
3959142424
3943711656
223708343758
17238536
2873
089394
2912
526377
84818401971
25410745
4235124242
3784364
058
235197175777
24660
44110
0666
6740
8185231
94929600332
2337
8527
3896
421212
3871699047
245013674745
23637727
3939621212
3937730937
104425304
818
2076
9664
346161060
63475
626276
25460
4622494
2139
2809
3565468182
3616
46205
1143046
8744
720112073
3352012121
3380893615
264715697984
22946236
3824372727
3703700535
12303026964
214473727
2412
287879
2379
968211
274767017231
2235
9845
37266
40909
37440
06578
International Journal of Distributed Sensor Networks 9
(b)Con
tinued
April
Day
Etc(mmsdotdıaminus1)
FuzzyIrrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
Day
Etc
(mmsdotdıaminus1)
FuzzyIrrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
134228506725
1981396
43302327273
3321061416
2849999503
2313
4864
385581060
63926951783
14419946964
121049973
3508328788
3298
255743
294685183652
2216
5509
3694
251515
3679
734635
1541340
0889
18688127
3114
687879
3246842989
303708190798
17870818
2978
469697
2912
406242
(c)
May
Day
Etc(mmsdotdıaminus1)
FuzzyIrrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
Day
Etc
(mmsdotdıaminus1)
Fuzzy
Irrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy
(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
134840
0241
16046282
2674
3803
273632909
14281323798
12056146
200935758
22095119
42
428945109
19954027
332567121
336892701
15345795001
15992027
266533788
2715
86759
3364
868417
17514855
2919
14242
286566985
16376349121
17962409
2993
73485
295583908
4400339841
190312
317186667
314426176
17341760316
1610
1982
268366364
268417925
527064
0388
1241364
62068940
912125604
6418
343767282
15971582
26619303
2699
94192
6351451778
16295346
2715
89091
276029581
19313612887
15119536
2519
92273
243822456
7329792176
1609100
9268183485
2590
1817
20293326251
14367546
2394
59091
246310986
8335111737
15496273
258271212
263196142
21305215788
13911227
2318
53788
230377899
9358129742
16387591
273126515
2812
7444
222
304
887854
14010273
233504545
2397
15919
10365825019
1766
4609
294410152
287318298
23252884747
1503564
62505940
912394
58361
11377619727
17437236
2906206
0629658184
24252884747
113733
1895
55198615216
12373897867
17908982
29848303
293658698
25320855932
1554804
62591
34091
2519
99659
13283378267
1312
6791
218779848
222564771
10 International Journal of Distributed Sensor Networks
Calculated volume (L)Supplied volume (L)
0500
100015002000250030003500400045005000
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Volu
me (
L)
Days (April)
Figure 12 Comparison between traditional irrigation and the fuzzysystem (April 2013)
Calculated volume (L)Supplied volume (L)
0500
1000150020002500300035004000
1 3 5 7 9 11 13 15 17 19 21 23 25
Volu
me (
L)
Days (May)
Figure 13 Comparison between traditional irrigation and the fuzzysystem (May 2013)
These results establish the reliability of the proposedsystem to provide adequate water and maintain optimummoisture level with low relative humidity to obtain thedesirable quality of hbanero pepper production
In thework ofOrtiz et al [20] the percentage of optimumsoil moisture for growing good quality fruits is 60 the fuzzysystem employs the percentage of 70 indicated by the expertso that it is possible to also set the reliability in the finalquality of the fruit The work presented by Perez-Gutierrezet al [19] mentioned that the volume of water applied to80 of potential evapotranspiration generates the amount ofwater in the soil to favor a constant process of transpirationfruit yield and improvement of water use this requires awater volume 2223m3 haminus1 however the fuzzy system onlyrequires 44995m3 haminus1 which represents a saving of 798water
The proposed system is a better form to schedule irriga-tion unlike Yao et al [16] the objective is to give to the poorcommunities a form to harvest a high demand product likehabanero pepper (with designation of origin) with enoughtechnologic structure The work of Yao et al [16] is using aneural network to refine the sprinkle time based on a patternthat is not described in the paper in a real environmentespecially in Yucatan Mexico the humidity and temperaturevariables have to be considered to obtain a quality product
02468
101214161820
000 224 448 712 936 1200 1424 1648 1912
Dur
atio
n of
irrig
atio
n (m
in)
Hours of day
Figure 14 Irrigation frequency using the fuzzy system (sample of20 March 2013)
40
35
30
25
20
15
10
5
0600 824 1048 1312 1536 1800
Hours of day
Tem
pera
ture
(∘C)
Figure 15 Temperature measurement (sample of 20 March 2013)
1009080706050403020100600 824 1048
Hours of day1312 1536 1800
Tem
pera
ture
(∘C)
Figure 16 Measurement of relative humidity (sample of 20 March2013)
and unlike the work of Yao et al [16] in this work are takeninto account in the fuzzy model and in the expert knowledgeThe use of a neural network is not a significant contributionfor managing the time of irrigation on a shadow housebecause there are no other important variables included andin the Yao et al [16] proposal only the soil moisture and theerror are considered to set the irrigation time
4 Conclusions
This paper has presented a fuzzy system that manages awireless irrigation scheme through an algorithm that takesinto account the conditions of microclimate of a shadowhouse used for growing habanero pepper with designation oforigin in the state of Yucatan The water volume consumed
International Journal of Distributed Sensor Networks 11
float h = dhtreadHumidity( )
float t = dhtreadTemperature()
revisa si retorna un valor valido de lo contrario hay un error
if (isnan (t) || isnan (h)) (
Serialprintln (Failed to read from DHT)
) else (
String hume = Humedad
Serialprintln (hume + h + de Humedad)
String tempe = Temperatura
Serialprintln (tempe + t + Grados Centrigrados )
)
Algorithm 2 Section of the Arduino code
SerialPort conectar = new SerialPort (COM4 9600)
conectarOpen( )
while ( shouldStop)
if (conectarBytesToRead gt 0)
string t = conectarReadLine( )
string h = conectarReadLine( )
string m = conectarReadLine( )
string texto
t = tReplace(r )
resp textText = t
hume textText = h
mov textText = m
if (tStartWith(Temperatura ))
Texto = t
t = tReplace(Temperatura )
else resp textText =
if (hStartWith(Humedad ))
texto = h
h = hReplace(Humedad )
else hume textText =
Algorithm 3 Application connection
according to the graphic that shows the comparison betweenthe volume of water from the system and the manual ortraditional volume gives us a difference of 798 over the bestresult shown in Perez-Gutierrez et al [19] which shows thatthe system can efficiently manage water and that incorpora-tion of the expert knowledge in fuzzy rules allows irrigationscheme without using the pan evaporation or calculating thevolume associated with irrigation as they can be automat-ically supplied without the crop being adversely affected inits development Saving water is about 1524823m3 for a cropof 1000 plants in an 85-day cycle causing transplantation atthe 30th day The microclimate conditions generated by theinfrastructure of shadow house are not exploited by the lack
of modernization that is a producer who does not have theability to use technology to irrigate their crops generally onlyconsidered during the morning or afternoon which doesnot guarantee the product quality since plants could fall ineither water stress or excess moisture However the proposedsystem allows for irrigation supply data from three commonsensors (temperature relative humidity and soil moisture)allowing similarly for reducing infrastructure costs
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
12 International Journal of Distributed Sensor Networks
public void bd tempe( )
SqlConnection con = new SqlConnection()
conConnectionString = Data Source=DAVIDSERVERSQLEXPRESS
Initial Catalog=ARDUINOIntegrated Security=True
try
conOpen( )
catch (SqlException ex)
MessageBoxShow(exMessage)
throw
string tiempo = DateTimeNowToString( )
SqlCommand instruccion = conCreateCommand( )
instruccionCommandText = insert into tempe(horatemperatura)
The authors gratefully acknowledge the support of the Tech-nological Institute of Conkal ofMexico and Prometeo Projectof the Secretariat for Higher Education Science Technologyand Innovation of the Republic of Ecuador and CYTEDnetwork 514RT0486
References
[1] FAOSTAT FAO Statistical Databases amp Data-Sets Food andAgriculture Organization of the United Nations 2012 (Consul-tado Dic 2013) httpfaostatfaoorgsite567DesktopDefaultaspxPageID=567ancor
[2] SIAP Agri-Food and Fishing Information and Statistics ServiceSAGARPA Anuario estadıstico de la Produccion AgrıcolaMexico City Mexico 2011
[3] USDA and FAS ldquoGreenhouse and shade house production tocontinue increasingrdquo GAIN Report MX0024 vol 22 USDAFAS Mexico DF Mexico 2010
[4] H Turral M Svendsen and J M Faures ldquoInvesting in irriga-tion reviewing the past and looking to the futurerdquo AgriculturalWater Management vol 97 no 4 pp 551ndash560 2010
[5] S Tang Q Zhu X Zhou S Liu and M Wu ldquoA conceptionof digital agriculturerdquo in Proceedings of the IEEE InternationalGeoscience and Remote Sensing Symposium (IGARSS rsquo02) vol5 pp 3026ndash3028 June 2002
[6] L Bacci P Battista and B Rapi ldquoAn integrated method forirrigation scheduling of potted plantsrdquo Scientia Horticulturaevol 116 no 1 pp 89ndash97 2008
[7] R Lopez Lopez R Arteaga Ramırez M A Vazquez Pena ILopez Cruz and I Sanchez Cohen ldquoIndice de estres hıdricocomo un indicador del momento de riego en cultivos agrıcolasrdquoAgricultura Tecnica en Mexico vol 35 no 1 pp 97ndash111 2009
[8] N Livellara F Saavedra and E Salgado ldquoPlant based indicatorsfor irrigation scheduling in young cherry treesrdquo AgriculturalWater Management vol 98 no 4 pp 684ndash690 2011
[9] R Qiu S Kang F Li et al ldquoEnergy partitioning and evapotran-spiration of hot pepper grown in greenhouse with furrow anddrip irrigation methodsrdquo Scientia Horticulturae vol 129 no 4pp 790ndash797 2011
[10] J Casadesus M Mata J Marsal and J Girona ldquoA generalalgorithm for automated scheduling of drip irrigation in treecropsrdquo Computers and Electronics in Agriculture vol 83 pp 11ndash20 2012
[11] C O Akinbile and M S Yusoff ldquoGrowth yield and water usepattern of chilli pepper under different irrigation schedulingand managementrdquo Asian Journal of Agricultural Research vol5 no 2 pp 154ndash163 2011
[12] Y Huang Y Lan S J Thomson A Fang W C Hoffmann andR E Lacey ldquoDevelopment of soft computing and applicationsin agricultural and biological engineeringrdquo Computers andElectronics in Agriculture vol 71 no 2 pp 107ndash127 2010
[13] M Omid M Lashgari H Mobli R Alimardani S Mohtasebiand R Hesamifard ldquoDesign of fuzzy logic control systemincorporating human expert knowledge for combine harvesterrdquoExpert Systems with Applications vol 37 no 10 pp 7080ndash70852010
[14] A Merot and J-E Bergez ldquoIRRIGATE a dynamic integratedmodel combining a knowledge-based model and mechanisticbiophysical models for border irrigation managementrdquo Envi-ronmental Modelling and Software vol 25 no 4 pp 421ndash4322010
[15] S L Davis and M D Dukes ldquoIrrigation scheduling perfor-mance by evapotranspiration-based controllersrdquo AgriculturalWater Management vol 98 no 1 pp 19ndash28 2010
[16] Z Yao G Lou Z XiuLi and Q Zhao ldquoResearch and devel-opment precision irrigation control system in agriculturalrdquoin Proceedings of the International Conference on Computerand Communication Technologies in Agriculture Engineering(CCTAE rsquo10) pp 117ndash120 June 2010
[17] E Garcia Modificaciones al Sistema de Clasificacion Climaticode Koppen Serie de Libros no 6 UNAM Instituto deGeografıaMexico City Mexico 5th edition 2004
International Journal of Distributed Sensor Networks 13
[18] K M Passino S Yurkovich and M Reinfrank Fuzzy Controlvol 42 Addison Wesley Longman Menlo Park Calif USA1998
[19] A Perez-Gutierrez A Pineda-Doporto L Latournerie-Moreno and C Godoy-Avila ldquoNiveles de evapotranspiracionpotencial en la produccion de chile habanerordquo Terra Latino-americana vol 26 no 1 pp 53ndash59 2008
[20] W C Q Ortiz A Perez-Gutierrez L L Moreno C May-LaraE R Sanchez and A J M Chacon ldquoUso de agua potencialhıdrico y rendimiento de chile habanero (Capsicum chinenseJacq)rdquo Revista Fitotecnia Mexicana vol 35 no 2 pp 155ndash1602012
International Journal of Distributed Sensor Networks 5
35
30
25
20
15
10
05
0100 20 30
Out
put v
olta
ge (V
)
40 50 60
Soil moisture ()
y = 42E minus 5x3 minus 00038x2 + 01265x + 00888R2 = 09974
Figure 3 Humidity sensor characterization
where 119860119895119894 119860119896
2 119860
119897
119899and 119861119904
119895for 119877119895 represent the corre-
sponding linguistic values [18]The processing block consistsof a hardware Arduino microcontroller which is the Atmel328p where the irrigation program established by the fuzzyrules is stored
232 Fuzzy Sets The linguistic variables to form the fuzzysets are as follows
(i) Temperature Due to the warm air in the shadow houseinfrastructure is retained and an important consideration inthe cultivation under protected conditions is the temperaturefactor because it favors the evaporation of water and has animportant impact in the crop The low technification of thisstructure only can provide air movement through the roofdoors or the antiaphid mesh around of him
(ii) RelativeHumidityThe humidity factor has great influenceon the crop Excess moisture in habanero pepper plantsaffects its development so it should not be given water to thecrop when the humidity is high however when the humiditydecreases it is necessary to supply water to the plant
(iii) Stages of the Crop Growth stages (119870119888) influence the devel-
opment of the plant especially considering that the habaneropepper is a plant that requires large amounts of water whichultimately affects the quality of the fruit This is an importantfactor for the habanero pepper because to obtain designationof origin you must have certain characteristics of the fruit atharvest
(iv) Irrigation Time The output of the fuzzy system is theirrigation time and represents the run time in minutes Thefrequency of watering time keeps the amount of water neededto avoid crop water stress
In order to convert the linguistic variables on fuzzynumbers this paper proposes the calculation of intermediatevalues of the linguistic range through the triangular function(4) while outliers are modeled by the Gamma function left
10
30 53 75 98 120
05
Growth
MaturityDevelopment
MaxMin
Figure 4 Input sets for the variable stage of development
part of the linguistic rang (5) and 119871 function right part of thelinguistic range (6)
120583 (119909 119886119898 119887)
= max min (119909 minus 119886) (119898 minus 119886) minus 1 (119887 minus 119909) (119887 minus 119898) minus 1 0 (4)
120583 (119909) =
0 119909 le 119886
(119909 minus 119886) (119898 minus 119886)minus1119909 isin (119886119898)
1 119909 ge 119898
(5)
The membership function is created with expert knowledgeand adopts a graphic form determined according to the typeof value associated with the fuzzy set The 119871 and Gammafunctions correspond to the membership functions whichare used to calculate extreme fuzzy values Therefore the 119871function is defined by
119871 = 1minusGamma (6)
The graphic form of the linguistic variables defined by thelast equations is shown in Figures 4 5 6 and 7
233 Inference Mechanism The inference mechanism hastwo basic tasks determines the relevance and extent of eachrule in relation to the current entries (119906
119894) and generates the
corresponding conclusions The combination of the sets ofrules with inputs can be calculated as follows
6 International Journal of Distributed Sensor Networks
10
05
130 175 220 265 310 355 400
MaxMin
Medium
Very fewFew Very high
High
Figure 5 Input sets for the variable relative humidity
10
05
300 4166 7666 8833 10005333 650
MaxMin
Medium
Very fewFew Very high
High
Figure 6 Input sets for the variable temperature
The result is a set with the ldquofiredrdquo rules To obtain a crispvalue that can be applied to the valve it will be necessary tocalculate the center of the graph and this can be done by
119910 =
int119910120583 (119910) 119910 119889119910
int119910120583 (119910) 119910 119889119910
(8)
24 Data Acquisition System The proposed communicationsystem includes a basic structure that can be used in agricul-tural environments and this proposal aims to organize thesections of a sensor network and facilitates the selection oftechnologies required to implement the ZigBee networkThecommunication system uses IEEE802154 protocol (ZigBee)which was implemented on an Arduino board with XBee Promodule of Maxtream configured with a PAN ID 3332 a rateof 9600 baud 8 data bits and no parity (see Figure 8)
The structure presented is divided into two sections aninternal for data collection (sensor network) and an exter-nal that can send information to central computers to storeandor process informationThe internal section is composedof elements that collect information from the agriculturalparameters of interest such as temperature relative humidityand soil moisture (sensors) the data collected will be sent todevices for processing through the ZigBee protocol which
10
05
0 333 667 100 1333 1667 200
Medium
Very shortShort Very long
Long
MaxMin
Figure 7 Output sets for the variable irrigation time
Network PAN id = 3332
CoordinatorDH 0DL FFFFMY 1
Fuzzy segmentDH 0DL 1MY A
Traditional segmentDH 0DL 1MY B
Figure 8 Sensor network
is used for transmission (end devices) The internal sectioncan be implemented through Arduino boards with Xbeemodules In the transmission of information the router nodewill receive the information of the end devices and will betransmitted to the coordinator for central processing in orderto be able to connect to a larger networkThe external sectionis composed of central processing devices sending data toremote nodes via Ethernet WiFi mobile devices or othermeans that can send information to other locations as shownin Figure 9
The data acquisition is made with a DuemilanovaArduino and this platform is programed for a sampling rateof 5 minutes Each value from the corresponding sensor hasan header for identification as shown in Algorithm 2 Thesedata are linked with the Xbee devices for the transmission tothe other Xbee configured like a coordinator
To get data from the serial port that the Xbee Shield(Coordinator) is sending we programmethods as can be seenin Algorithm 3 and the header of each data frame is definedso that it recognizes and takes the indicated action for storageat the database every time that receives data (Algorithm 4)
3 Results and Discussion
The behavior of the proposed system was supervised fromMarch to May 2013 this corresponds to all cycle of cropTables 4(a) 4(b) and 4(c) show the average values acquiredfrom the system The results obtained are compared with
International Journal of Distributed Sensor Networks 7
Shaded houseinfrastructure
Sensors
Sensors
SensorsSensors
SensorsSensors
External section
ZigBee
ZigBeeZigBee
coordinator
end device
ZigBeeend device
Internal section
router
GPRS Ethernet WiFi
Centralprocessing
Arduino + Xbee
Arduino + Xbee
50m
Figure 9 Internal and external sections
65432100 10 20 30 40 50 60 70
Days after transplant
Etc (
mm
)
Figure 10 Evapotranspiration values for the crop cycle
the results of calculations performed for the water loss byevapotranspiration reference (Eto) as shown in the work ofPerez-Gutierrez et al [19] The comparison between fuzzysystems of irrigation and traditional irrigationwas performedto validate the data obtained
The Etc shows values that are in an interval of 3mmsdotdayminus1(Figure 10) This range is stable for all the harvest period ifvariations of the Etc were more spacious and then shouldtake into account a greater number of variables thereforeby maintaining a controlled environment with the use of theshadow house we can use a small number of sensors and inthis proposal the use of three of them is enough to estimatethe irrigation
The system showed a deviation of +011 in Marchminus039 in April and +051 in May (Figures 11 12 and13) The supplied volume by the fuzzy system is comparedwith the calculated volume resulting in a small deviationtherefore we can establish the reliability of the system
0500
10001500200025003000350040004500
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
Volu
me (
L)
Days (March)
Calculated volume (L)Supplied volume (L)
Figure 11 Comparison between traditional irrigation and the fuzzysystem (March 2013)
With the fuzzy system validated the analysis of the datafor eachmonth can be made As part of the validation sampleMarch 20 is shown In Figure 14 the frequency of irrigationduring the day is graphed
According to the data obtained with respect to thefrequency of irrigation Figure 14 shows that the frequencyof irrigation is higher in the time period between 1030and 1300 in this period we can observe an increase trendin the temperature (Figure 15) and a low relative humidity(Figure 16) indicating a water accumulation in the soiltherefore a balance between evapotranspiration and thewater applied to the crop is generated
8 International Journal of Distributed Sensor Networks
Table4(a)D
atafrom
them
onth
ofMarch
forthe
cycle
ofcrop(b)
Datafrom
them
onth
ofAp
rilforthe
cycle
ofcrop(c)Datafrom
them
onth
ofMay
forthe
cycle
ofcrop
(a)
March
Day
Etc(mmsdotdıaminus1 )
FuzzyIrrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
Day
Etc
(mmsdotdıaminus1)
Fuzzy
Irrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy
(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
2258425473
1341340
92235568182
2029668918
17357783512
168119
182801986364
2810
025131
3257725364
10913627
1818
937879
2024170272
18307273124
14404782
240079697
2413
317471
437044
9566
15902236
2650372727
2909504
086
19373218858
17796109
2966018182
2931254056
5384566297
182036
3033933333
3020376637
2039600
9363
18436127
3072
687879
3110
250267
6333154632
149705
2495
083333
2616
59036
2134154618
15833791
2638965152
2682497426
7307303393
14569518
2472
201515
2413
555204
22444
59937
22096245
3682707576
3491
875286
837064
8946
17890227
2981704545
2911070015
23450743937
2116
9736
3528289394
3540134599
93627264
2817962364
2993
727273
2848846
704
24467591342
21811636
3635272727
3672
453809
10383003911
1852
4945
3174
921212
3008105683
2513
804632
566
89909
9448318182
1084213265
112655606
8411413364
1902227273
2085708733
26096752606
45595818
759930303
7598931903
1224746
8837
11749273
1999
775758
1943615697
27366104898
17867809
2977
968182
287538114
113
231084615
1112
2518
185375303
1814
934321
28346
753609
1576
0455
2626742424
2723396
476
14242255034
10806
673
1801112121
1902666591
29460879854
22293755
3715
625758
3619
741905
15349001336
1674
8164
2791
3606
062741050084
30462318448
21561836
3593
639394
3631040
596
16369959684
17351655
2891
942424
2905656565
31488327963
22941936
3823656061
3835318856
(b)
April
Day
Etc(mmsdotdıaminus1)
FuzzyIrrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
Day
Etc
(mmsdotdıaminus1)
FuzzyIrrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
13961718653
18771482
3128580303
3111526554
16399938767
17371336
2895
222727
31411117
32
4169559209
25227564
4204593939
3274
764145
17409686903
192428
3207133333
3217
673412
3404
8530602
18909436
3151572727
3179
708499
18471907325
21807836
3634639394
370635146
44
3418113126
15290055
2548342424
2684579772
195057651274
22931773
3821962121
3972
270022
54344343997
20729182
3454863636
3412
039796
203687610895
14040
036
23400
06061
2896
242824
652164
59781
24479782
4079
963636
4096
997931
214583233577
21846
064
364101060
63599
663234
75021289634
23754855
3959142424
3943711656
223708343758
17238536
2873
089394
2912
526377
84818401971
25410745
4235124242
3784364
058
235197175777
24660
44110
0666
6740
8185231
94929600332
2337
8527
3896
421212
3871699047
245013674745
23637727
3939621212
3937730937
104425304
818
2076
9664
346161060
63475
626276
25460
4622494
2139
2809
3565468182
3616
46205
1143046
8744
720112073
3352012121
3380893615
264715697984
22946236
3824372727
3703700535
12303026964
214473727
2412
287879
2379
968211
274767017231
2235
9845
37266
40909
37440
06578
International Journal of Distributed Sensor Networks 9
(b)Con
tinued
April
Day
Etc(mmsdotdıaminus1)
FuzzyIrrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
Day
Etc
(mmsdotdıaminus1)
FuzzyIrrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
134228506725
1981396
43302327273
3321061416
2849999503
2313
4864
385581060
63926951783
14419946964
121049973
3508328788
3298
255743
294685183652
2216
5509
3694
251515
3679
734635
1541340
0889
18688127
3114
687879
3246842989
303708190798
17870818
2978
469697
2912
406242
(c)
May
Day
Etc(mmsdotdıaminus1)
FuzzyIrrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
Day
Etc
(mmsdotdıaminus1)
Fuzzy
Irrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy
(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
134840
0241
16046282
2674
3803
273632909
14281323798
12056146
200935758
22095119
42
428945109
19954027
332567121
336892701
15345795001
15992027
266533788
2715
86759
3364
868417
17514855
2919
14242
286566985
16376349121
17962409
2993
73485
295583908
4400339841
190312
317186667
314426176
17341760316
1610
1982
268366364
268417925
527064
0388
1241364
62068940
912125604
6418
343767282
15971582
26619303
2699
94192
6351451778
16295346
2715
89091
276029581
19313612887
15119536
2519
92273
243822456
7329792176
1609100
9268183485
2590
1817
20293326251
14367546
2394
59091
246310986
8335111737
15496273
258271212
263196142
21305215788
13911227
2318
53788
230377899
9358129742
16387591
273126515
2812
7444
222
304
887854
14010273
233504545
2397
15919
10365825019
1766
4609
294410152
287318298
23252884747
1503564
62505940
912394
58361
11377619727
17437236
2906206
0629658184
24252884747
113733
1895
55198615216
12373897867
17908982
29848303
293658698
25320855932
1554804
62591
34091
2519
99659
13283378267
1312
6791
218779848
222564771
10 International Journal of Distributed Sensor Networks
Calculated volume (L)Supplied volume (L)
0500
100015002000250030003500400045005000
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Volu
me (
L)
Days (April)
Figure 12 Comparison between traditional irrigation and the fuzzysystem (April 2013)
Calculated volume (L)Supplied volume (L)
0500
1000150020002500300035004000
1 3 5 7 9 11 13 15 17 19 21 23 25
Volu
me (
L)
Days (May)
Figure 13 Comparison between traditional irrigation and the fuzzysystem (May 2013)
These results establish the reliability of the proposedsystem to provide adequate water and maintain optimummoisture level with low relative humidity to obtain thedesirable quality of hbanero pepper production
In thework ofOrtiz et al [20] the percentage of optimumsoil moisture for growing good quality fruits is 60 the fuzzysystem employs the percentage of 70 indicated by the expertso that it is possible to also set the reliability in the finalquality of the fruit The work presented by Perez-Gutierrezet al [19] mentioned that the volume of water applied to80 of potential evapotranspiration generates the amount ofwater in the soil to favor a constant process of transpirationfruit yield and improvement of water use this requires awater volume 2223m3 haminus1 however the fuzzy system onlyrequires 44995m3 haminus1 which represents a saving of 798water
The proposed system is a better form to schedule irriga-tion unlike Yao et al [16] the objective is to give to the poorcommunities a form to harvest a high demand product likehabanero pepper (with designation of origin) with enoughtechnologic structure The work of Yao et al [16] is using aneural network to refine the sprinkle time based on a patternthat is not described in the paper in a real environmentespecially in Yucatan Mexico the humidity and temperaturevariables have to be considered to obtain a quality product
02468
101214161820
000 224 448 712 936 1200 1424 1648 1912
Dur
atio
n of
irrig
atio
n (m
in)
Hours of day
Figure 14 Irrigation frequency using the fuzzy system (sample of20 March 2013)
40
35
30
25
20
15
10
5
0600 824 1048 1312 1536 1800
Hours of day
Tem
pera
ture
(∘C)
Figure 15 Temperature measurement (sample of 20 March 2013)
1009080706050403020100600 824 1048
Hours of day1312 1536 1800
Tem
pera
ture
(∘C)
Figure 16 Measurement of relative humidity (sample of 20 March2013)
and unlike the work of Yao et al [16] in this work are takeninto account in the fuzzy model and in the expert knowledgeThe use of a neural network is not a significant contributionfor managing the time of irrigation on a shadow housebecause there are no other important variables included andin the Yao et al [16] proposal only the soil moisture and theerror are considered to set the irrigation time
4 Conclusions
This paper has presented a fuzzy system that manages awireless irrigation scheme through an algorithm that takesinto account the conditions of microclimate of a shadowhouse used for growing habanero pepper with designation oforigin in the state of Yucatan The water volume consumed
International Journal of Distributed Sensor Networks 11
float h = dhtreadHumidity( )
float t = dhtreadTemperature()
revisa si retorna un valor valido de lo contrario hay un error
if (isnan (t) || isnan (h)) (
Serialprintln (Failed to read from DHT)
) else (
String hume = Humedad
Serialprintln (hume + h + de Humedad)
String tempe = Temperatura
Serialprintln (tempe + t + Grados Centrigrados )
)
Algorithm 2 Section of the Arduino code
SerialPort conectar = new SerialPort (COM4 9600)
conectarOpen( )
while ( shouldStop)
if (conectarBytesToRead gt 0)
string t = conectarReadLine( )
string h = conectarReadLine( )
string m = conectarReadLine( )
string texto
t = tReplace(r )
resp textText = t
hume textText = h
mov textText = m
if (tStartWith(Temperatura ))
Texto = t
t = tReplace(Temperatura )
else resp textText =
if (hStartWith(Humedad ))
texto = h
h = hReplace(Humedad )
else hume textText =
Algorithm 3 Application connection
according to the graphic that shows the comparison betweenthe volume of water from the system and the manual ortraditional volume gives us a difference of 798 over the bestresult shown in Perez-Gutierrez et al [19] which shows thatthe system can efficiently manage water and that incorpora-tion of the expert knowledge in fuzzy rules allows irrigationscheme without using the pan evaporation or calculating thevolume associated with irrigation as they can be automat-ically supplied without the crop being adversely affected inits development Saving water is about 1524823m3 for a cropof 1000 plants in an 85-day cycle causing transplantation atthe 30th day The microclimate conditions generated by theinfrastructure of shadow house are not exploited by the lack
of modernization that is a producer who does not have theability to use technology to irrigate their crops generally onlyconsidered during the morning or afternoon which doesnot guarantee the product quality since plants could fall ineither water stress or excess moisture However the proposedsystem allows for irrigation supply data from three commonsensors (temperature relative humidity and soil moisture)allowing similarly for reducing infrastructure costs
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
12 International Journal of Distributed Sensor Networks
public void bd tempe( )
SqlConnection con = new SqlConnection()
conConnectionString = Data Source=DAVIDSERVERSQLEXPRESS
Initial Catalog=ARDUINOIntegrated Security=True
try
conOpen( )
catch (SqlException ex)
MessageBoxShow(exMessage)
throw
string tiempo = DateTimeNowToString( )
SqlCommand instruccion = conCreateCommand( )
instruccionCommandText = insert into tempe(horatemperatura)
The authors gratefully acknowledge the support of the Tech-nological Institute of Conkal ofMexico and Prometeo Projectof the Secretariat for Higher Education Science Technologyand Innovation of the Republic of Ecuador and CYTEDnetwork 514RT0486
References
[1] FAOSTAT FAO Statistical Databases amp Data-Sets Food andAgriculture Organization of the United Nations 2012 (Consul-tado Dic 2013) httpfaostatfaoorgsite567DesktopDefaultaspxPageID=567ancor
[2] SIAP Agri-Food and Fishing Information and Statistics ServiceSAGARPA Anuario estadıstico de la Produccion AgrıcolaMexico City Mexico 2011
[3] USDA and FAS ldquoGreenhouse and shade house production tocontinue increasingrdquo GAIN Report MX0024 vol 22 USDAFAS Mexico DF Mexico 2010
[4] H Turral M Svendsen and J M Faures ldquoInvesting in irriga-tion reviewing the past and looking to the futurerdquo AgriculturalWater Management vol 97 no 4 pp 551ndash560 2010
[5] S Tang Q Zhu X Zhou S Liu and M Wu ldquoA conceptionof digital agriculturerdquo in Proceedings of the IEEE InternationalGeoscience and Remote Sensing Symposium (IGARSS rsquo02) vol5 pp 3026ndash3028 June 2002
[6] L Bacci P Battista and B Rapi ldquoAn integrated method forirrigation scheduling of potted plantsrdquo Scientia Horticulturaevol 116 no 1 pp 89ndash97 2008
[7] R Lopez Lopez R Arteaga Ramırez M A Vazquez Pena ILopez Cruz and I Sanchez Cohen ldquoIndice de estres hıdricocomo un indicador del momento de riego en cultivos agrıcolasrdquoAgricultura Tecnica en Mexico vol 35 no 1 pp 97ndash111 2009
[8] N Livellara F Saavedra and E Salgado ldquoPlant based indicatorsfor irrigation scheduling in young cherry treesrdquo AgriculturalWater Management vol 98 no 4 pp 684ndash690 2011
[9] R Qiu S Kang F Li et al ldquoEnergy partitioning and evapotran-spiration of hot pepper grown in greenhouse with furrow anddrip irrigation methodsrdquo Scientia Horticulturae vol 129 no 4pp 790ndash797 2011
[10] J Casadesus M Mata J Marsal and J Girona ldquoA generalalgorithm for automated scheduling of drip irrigation in treecropsrdquo Computers and Electronics in Agriculture vol 83 pp 11ndash20 2012
[11] C O Akinbile and M S Yusoff ldquoGrowth yield and water usepattern of chilli pepper under different irrigation schedulingand managementrdquo Asian Journal of Agricultural Research vol5 no 2 pp 154ndash163 2011
[12] Y Huang Y Lan S J Thomson A Fang W C Hoffmann andR E Lacey ldquoDevelopment of soft computing and applicationsin agricultural and biological engineeringrdquo Computers andElectronics in Agriculture vol 71 no 2 pp 107ndash127 2010
[13] M Omid M Lashgari H Mobli R Alimardani S Mohtasebiand R Hesamifard ldquoDesign of fuzzy logic control systemincorporating human expert knowledge for combine harvesterrdquoExpert Systems with Applications vol 37 no 10 pp 7080ndash70852010
[14] A Merot and J-E Bergez ldquoIRRIGATE a dynamic integratedmodel combining a knowledge-based model and mechanisticbiophysical models for border irrigation managementrdquo Envi-ronmental Modelling and Software vol 25 no 4 pp 421ndash4322010
[15] S L Davis and M D Dukes ldquoIrrigation scheduling perfor-mance by evapotranspiration-based controllersrdquo AgriculturalWater Management vol 98 no 1 pp 19ndash28 2010
[16] Z Yao G Lou Z XiuLi and Q Zhao ldquoResearch and devel-opment precision irrigation control system in agriculturalrdquoin Proceedings of the International Conference on Computerand Communication Technologies in Agriculture Engineering(CCTAE rsquo10) pp 117ndash120 June 2010
[17] E Garcia Modificaciones al Sistema de Clasificacion Climaticode Koppen Serie de Libros no 6 UNAM Instituto deGeografıaMexico City Mexico 5th edition 2004
International Journal of Distributed Sensor Networks 13
[18] K M Passino S Yurkovich and M Reinfrank Fuzzy Controlvol 42 Addison Wesley Longman Menlo Park Calif USA1998
[19] A Perez-Gutierrez A Pineda-Doporto L Latournerie-Moreno and C Godoy-Avila ldquoNiveles de evapotranspiracionpotencial en la produccion de chile habanerordquo Terra Latino-americana vol 26 no 1 pp 53ndash59 2008
[20] W C Q Ortiz A Perez-Gutierrez L L Moreno C May-LaraE R Sanchez and A J M Chacon ldquoUso de agua potencialhıdrico y rendimiento de chile habanero (Capsicum chinenseJacq)rdquo Revista Fitotecnia Mexicana vol 35 no 2 pp 155ndash1602012
6 International Journal of Distributed Sensor Networks
10
05
130 175 220 265 310 355 400
MaxMin
Medium
Very fewFew Very high
High
Figure 5 Input sets for the variable relative humidity
10
05
300 4166 7666 8833 10005333 650
MaxMin
Medium
Very fewFew Very high
High
Figure 6 Input sets for the variable temperature
The result is a set with the ldquofiredrdquo rules To obtain a crispvalue that can be applied to the valve it will be necessary tocalculate the center of the graph and this can be done by
119910 =
int119910120583 (119910) 119910 119889119910
int119910120583 (119910) 119910 119889119910
(8)
24 Data Acquisition System The proposed communicationsystem includes a basic structure that can be used in agricul-tural environments and this proposal aims to organize thesections of a sensor network and facilitates the selection oftechnologies required to implement the ZigBee networkThecommunication system uses IEEE802154 protocol (ZigBee)which was implemented on an Arduino board with XBee Promodule of Maxtream configured with a PAN ID 3332 a rateof 9600 baud 8 data bits and no parity (see Figure 8)
The structure presented is divided into two sections aninternal for data collection (sensor network) and an exter-nal that can send information to central computers to storeandor process informationThe internal section is composedof elements that collect information from the agriculturalparameters of interest such as temperature relative humidityand soil moisture (sensors) the data collected will be sent todevices for processing through the ZigBee protocol which
10
05
0 333 667 100 1333 1667 200
Medium
Very shortShort Very long
Long
MaxMin
Figure 7 Output sets for the variable irrigation time
Network PAN id = 3332
CoordinatorDH 0DL FFFFMY 1
Fuzzy segmentDH 0DL 1MY A
Traditional segmentDH 0DL 1MY B
Figure 8 Sensor network
is used for transmission (end devices) The internal sectioncan be implemented through Arduino boards with Xbeemodules In the transmission of information the router nodewill receive the information of the end devices and will betransmitted to the coordinator for central processing in orderto be able to connect to a larger networkThe external sectionis composed of central processing devices sending data toremote nodes via Ethernet WiFi mobile devices or othermeans that can send information to other locations as shownin Figure 9
The data acquisition is made with a DuemilanovaArduino and this platform is programed for a sampling rateof 5 minutes Each value from the corresponding sensor hasan header for identification as shown in Algorithm 2 Thesedata are linked with the Xbee devices for the transmission tothe other Xbee configured like a coordinator
To get data from the serial port that the Xbee Shield(Coordinator) is sending we programmethods as can be seenin Algorithm 3 and the header of each data frame is definedso that it recognizes and takes the indicated action for storageat the database every time that receives data (Algorithm 4)
3 Results and Discussion
The behavior of the proposed system was supervised fromMarch to May 2013 this corresponds to all cycle of cropTables 4(a) 4(b) and 4(c) show the average values acquiredfrom the system The results obtained are compared with
International Journal of Distributed Sensor Networks 7
Shaded houseinfrastructure
Sensors
Sensors
SensorsSensors
SensorsSensors
External section
ZigBee
ZigBeeZigBee
coordinator
end device
ZigBeeend device
Internal section
router
GPRS Ethernet WiFi
Centralprocessing
Arduino + Xbee
Arduino + Xbee
50m
Figure 9 Internal and external sections
65432100 10 20 30 40 50 60 70
Days after transplant
Etc (
mm
)
Figure 10 Evapotranspiration values for the crop cycle
the results of calculations performed for the water loss byevapotranspiration reference (Eto) as shown in the work ofPerez-Gutierrez et al [19] The comparison between fuzzysystems of irrigation and traditional irrigationwas performedto validate the data obtained
The Etc shows values that are in an interval of 3mmsdotdayminus1(Figure 10) This range is stable for all the harvest period ifvariations of the Etc were more spacious and then shouldtake into account a greater number of variables thereforeby maintaining a controlled environment with the use of theshadow house we can use a small number of sensors and inthis proposal the use of three of them is enough to estimatethe irrigation
The system showed a deviation of +011 in Marchminus039 in April and +051 in May (Figures 11 12 and13) The supplied volume by the fuzzy system is comparedwith the calculated volume resulting in a small deviationtherefore we can establish the reliability of the system
0500
10001500200025003000350040004500
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
Volu
me (
L)
Days (March)
Calculated volume (L)Supplied volume (L)
Figure 11 Comparison between traditional irrigation and the fuzzysystem (March 2013)
With the fuzzy system validated the analysis of the datafor eachmonth can be made As part of the validation sampleMarch 20 is shown In Figure 14 the frequency of irrigationduring the day is graphed
According to the data obtained with respect to thefrequency of irrigation Figure 14 shows that the frequencyof irrigation is higher in the time period between 1030and 1300 in this period we can observe an increase trendin the temperature (Figure 15) and a low relative humidity(Figure 16) indicating a water accumulation in the soiltherefore a balance between evapotranspiration and thewater applied to the crop is generated
8 International Journal of Distributed Sensor Networks
Table4(a)D
atafrom
them
onth
ofMarch
forthe
cycle
ofcrop(b)
Datafrom
them
onth
ofAp
rilforthe
cycle
ofcrop(c)Datafrom
them
onth
ofMay
forthe
cycle
ofcrop
(a)
March
Day
Etc(mmsdotdıaminus1 )
FuzzyIrrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
Day
Etc
(mmsdotdıaminus1)
Fuzzy
Irrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy
(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
2258425473
1341340
92235568182
2029668918
17357783512
168119
182801986364
2810
025131
3257725364
10913627
1818
937879
2024170272
18307273124
14404782
240079697
2413
317471
437044
9566
15902236
2650372727
2909504
086
19373218858
17796109
2966018182
2931254056
5384566297
182036
3033933333
3020376637
2039600
9363
18436127
3072
687879
3110
250267
6333154632
149705
2495
083333
2616
59036
2134154618
15833791
2638965152
2682497426
7307303393
14569518
2472
201515
2413
555204
22444
59937
22096245
3682707576
3491
875286
837064
8946
17890227
2981704545
2911070015
23450743937
2116
9736
3528289394
3540134599
93627264
2817962364
2993
727273
2848846
704
24467591342
21811636
3635272727
3672
453809
10383003911
1852
4945
3174
921212
3008105683
2513
804632
566
89909
9448318182
1084213265
112655606
8411413364
1902227273
2085708733
26096752606
45595818
759930303
7598931903
1224746
8837
11749273
1999
775758
1943615697
27366104898
17867809
2977
968182
287538114
113
231084615
1112
2518
185375303
1814
934321
28346
753609
1576
0455
2626742424
2723396
476
14242255034
10806
673
1801112121
1902666591
29460879854
22293755
3715
625758
3619
741905
15349001336
1674
8164
2791
3606
062741050084
30462318448
21561836
3593
639394
3631040
596
16369959684
17351655
2891
942424
2905656565
31488327963
22941936
3823656061
3835318856
(b)
April
Day
Etc(mmsdotdıaminus1)
FuzzyIrrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
Day
Etc
(mmsdotdıaminus1)
FuzzyIrrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
13961718653
18771482
3128580303
3111526554
16399938767
17371336
2895
222727
31411117
32
4169559209
25227564
4204593939
3274
764145
17409686903
192428
3207133333
3217
673412
3404
8530602
18909436
3151572727
3179
708499
18471907325
21807836
3634639394
370635146
44
3418113126
15290055
2548342424
2684579772
195057651274
22931773
3821962121
3972
270022
54344343997
20729182
3454863636
3412
039796
203687610895
14040
036
23400
06061
2896
242824
652164
59781
24479782
4079
963636
4096
997931
214583233577
21846
064
364101060
63599
663234
75021289634
23754855
3959142424
3943711656
223708343758
17238536
2873
089394
2912
526377
84818401971
25410745
4235124242
3784364
058
235197175777
24660
44110
0666
6740
8185231
94929600332
2337
8527
3896
421212
3871699047
245013674745
23637727
3939621212
3937730937
104425304
818
2076
9664
346161060
63475
626276
25460
4622494
2139
2809
3565468182
3616
46205
1143046
8744
720112073
3352012121
3380893615
264715697984
22946236
3824372727
3703700535
12303026964
214473727
2412
287879
2379
968211
274767017231
2235
9845
37266
40909
37440
06578
International Journal of Distributed Sensor Networks 9
(b)Con
tinued
April
Day
Etc(mmsdotdıaminus1)
FuzzyIrrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
Day
Etc
(mmsdotdıaminus1)
FuzzyIrrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
134228506725
1981396
43302327273
3321061416
2849999503
2313
4864
385581060
63926951783
14419946964
121049973
3508328788
3298
255743
294685183652
2216
5509
3694
251515
3679
734635
1541340
0889
18688127
3114
687879
3246842989
303708190798
17870818
2978
469697
2912
406242
(c)
May
Day
Etc(mmsdotdıaminus1)
FuzzyIrrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
Day
Etc
(mmsdotdıaminus1)
Fuzzy
Irrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy
(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
134840
0241
16046282
2674
3803
273632909
14281323798
12056146
200935758
22095119
42
428945109
19954027
332567121
336892701
15345795001
15992027
266533788
2715
86759
3364
868417
17514855
2919
14242
286566985
16376349121
17962409
2993
73485
295583908
4400339841
190312
317186667
314426176
17341760316
1610
1982
268366364
268417925
527064
0388
1241364
62068940
912125604
6418
343767282
15971582
26619303
2699
94192
6351451778
16295346
2715
89091
276029581
19313612887
15119536
2519
92273
243822456
7329792176
1609100
9268183485
2590
1817
20293326251
14367546
2394
59091
246310986
8335111737
15496273
258271212
263196142
21305215788
13911227
2318
53788
230377899
9358129742
16387591
273126515
2812
7444
222
304
887854
14010273
233504545
2397
15919
10365825019
1766
4609
294410152
287318298
23252884747
1503564
62505940
912394
58361
11377619727
17437236
2906206
0629658184
24252884747
113733
1895
55198615216
12373897867
17908982
29848303
293658698
25320855932
1554804
62591
34091
2519
99659
13283378267
1312
6791
218779848
222564771
10 International Journal of Distributed Sensor Networks
Calculated volume (L)Supplied volume (L)
0500
100015002000250030003500400045005000
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Volu
me (
L)
Days (April)
Figure 12 Comparison between traditional irrigation and the fuzzysystem (April 2013)
Calculated volume (L)Supplied volume (L)
0500
1000150020002500300035004000
1 3 5 7 9 11 13 15 17 19 21 23 25
Volu
me (
L)
Days (May)
Figure 13 Comparison between traditional irrigation and the fuzzysystem (May 2013)
These results establish the reliability of the proposedsystem to provide adequate water and maintain optimummoisture level with low relative humidity to obtain thedesirable quality of hbanero pepper production
In thework ofOrtiz et al [20] the percentage of optimumsoil moisture for growing good quality fruits is 60 the fuzzysystem employs the percentage of 70 indicated by the expertso that it is possible to also set the reliability in the finalquality of the fruit The work presented by Perez-Gutierrezet al [19] mentioned that the volume of water applied to80 of potential evapotranspiration generates the amount ofwater in the soil to favor a constant process of transpirationfruit yield and improvement of water use this requires awater volume 2223m3 haminus1 however the fuzzy system onlyrequires 44995m3 haminus1 which represents a saving of 798water
The proposed system is a better form to schedule irriga-tion unlike Yao et al [16] the objective is to give to the poorcommunities a form to harvest a high demand product likehabanero pepper (with designation of origin) with enoughtechnologic structure The work of Yao et al [16] is using aneural network to refine the sprinkle time based on a patternthat is not described in the paper in a real environmentespecially in Yucatan Mexico the humidity and temperaturevariables have to be considered to obtain a quality product
02468
101214161820
000 224 448 712 936 1200 1424 1648 1912
Dur
atio
n of
irrig
atio
n (m
in)
Hours of day
Figure 14 Irrigation frequency using the fuzzy system (sample of20 March 2013)
40
35
30
25
20
15
10
5
0600 824 1048 1312 1536 1800
Hours of day
Tem
pera
ture
(∘C)
Figure 15 Temperature measurement (sample of 20 March 2013)
1009080706050403020100600 824 1048
Hours of day1312 1536 1800
Tem
pera
ture
(∘C)
Figure 16 Measurement of relative humidity (sample of 20 March2013)
and unlike the work of Yao et al [16] in this work are takeninto account in the fuzzy model and in the expert knowledgeThe use of a neural network is not a significant contributionfor managing the time of irrigation on a shadow housebecause there are no other important variables included andin the Yao et al [16] proposal only the soil moisture and theerror are considered to set the irrigation time
4 Conclusions
This paper has presented a fuzzy system that manages awireless irrigation scheme through an algorithm that takesinto account the conditions of microclimate of a shadowhouse used for growing habanero pepper with designation oforigin in the state of Yucatan The water volume consumed
International Journal of Distributed Sensor Networks 11
float h = dhtreadHumidity( )
float t = dhtreadTemperature()
revisa si retorna un valor valido de lo contrario hay un error
if (isnan (t) || isnan (h)) (
Serialprintln (Failed to read from DHT)
) else (
String hume = Humedad
Serialprintln (hume + h + de Humedad)
String tempe = Temperatura
Serialprintln (tempe + t + Grados Centrigrados )
)
Algorithm 2 Section of the Arduino code
SerialPort conectar = new SerialPort (COM4 9600)
conectarOpen( )
while ( shouldStop)
if (conectarBytesToRead gt 0)
string t = conectarReadLine( )
string h = conectarReadLine( )
string m = conectarReadLine( )
string texto
t = tReplace(r )
resp textText = t
hume textText = h
mov textText = m
if (tStartWith(Temperatura ))
Texto = t
t = tReplace(Temperatura )
else resp textText =
if (hStartWith(Humedad ))
texto = h
h = hReplace(Humedad )
else hume textText =
Algorithm 3 Application connection
according to the graphic that shows the comparison betweenthe volume of water from the system and the manual ortraditional volume gives us a difference of 798 over the bestresult shown in Perez-Gutierrez et al [19] which shows thatthe system can efficiently manage water and that incorpora-tion of the expert knowledge in fuzzy rules allows irrigationscheme without using the pan evaporation or calculating thevolume associated with irrigation as they can be automat-ically supplied without the crop being adversely affected inits development Saving water is about 1524823m3 for a cropof 1000 plants in an 85-day cycle causing transplantation atthe 30th day The microclimate conditions generated by theinfrastructure of shadow house are not exploited by the lack
of modernization that is a producer who does not have theability to use technology to irrigate their crops generally onlyconsidered during the morning or afternoon which doesnot guarantee the product quality since plants could fall ineither water stress or excess moisture However the proposedsystem allows for irrigation supply data from three commonsensors (temperature relative humidity and soil moisture)allowing similarly for reducing infrastructure costs
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
12 International Journal of Distributed Sensor Networks
public void bd tempe( )
SqlConnection con = new SqlConnection()
conConnectionString = Data Source=DAVIDSERVERSQLEXPRESS
Initial Catalog=ARDUINOIntegrated Security=True
try
conOpen( )
catch (SqlException ex)
MessageBoxShow(exMessage)
throw
string tiempo = DateTimeNowToString( )
SqlCommand instruccion = conCreateCommand( )
instruccionCommandText = insert into tempe(horatemperatura)
The authors gratefully acknowledge the support of the Tech-nological Institute of Conkal ofMexico and Prometeo Projectof the Secretariat for Higher Education Science Technologyand Innovation of the Republic of Ecuador and CYTEDnetwork 514RT0486
References
[1] FAOSTAT FAO Statistical Databases amp Data-Sets Food andAgriculture Organization of the United Nations 2012 (Consul-tado Dic 2013) httpfaostatfaoorgsite567DesktopDefaultaspxPageID=567ancor
[2] SIAP Agri-Food and Fishing Information and Statistics ServiceSAGARPA Anuario estadıstico de la Produccion AgrıcolaMexico City Mexico 2011
[3] USDA and FAS ldquoGreenhouse and shade house production tocontinue increasingrdquo GAIN Report MX0024 vol 22 USDAFAS Mexico DF Mexico 2010
[4] H Turral M Svendsen and J M Faures ldquoInvesting in irriga-tion reviewing the past and looking to the futurerdquo AgriculturalWater Management vol 97 no 4 pp 551ndash560 2010
[5] S Tang Q Zhu X Zhou S Liu and M Wu ldquoA conceptionof digital agriculturerdquo in Proceedings of the IEEE InternationalGeoscience and Remote Sensing Symposium (IGARSS rsquo02) vol5 pp 3026ndash3028 June 2002
[6] L Bacci P Battista and B Rapi ldquoAn integrated method forirrigation scheduling of potted plantsrdquo Scientia Horticulturaevol 116 no 1 pp 89ndash97 2008
[7] R Lopez Lopez R Arteaga Ramırez M A Vazquez Pena ILopez Cruz and I Sanchez Cohen ldquoIndice de estres hıdricocomo un indicador del momento de riego en cultivos agrıcolasrdquoAgricultura Tecnica en Mexico vol 35 no 1 pp 97ndash111 2009
[8] N Livellara F Saavedra and E Salgado ldquoPlant based indicatorsfor irrigation scheduling in young cherry treesrdquo AgriculturalWater Management vol 98 no 4 pp 684ndash690 2011
[9] R Qiu S Kang F Li et al ldquoEnergy partitioning and evapotran-spiration of hot pepper grown in greenhouse with furrow anddrip irrigation methodsrdquo Scientia Horticulturae vol 129 no 4pp 790ndash797 2011
[10] J Casadesus M Mata J Marsal and J Girona ldquoA generalalgorithm for automated scheduling of drip irrigation in treecropsrdquo Computers and Electronics in Agriculture vol 83 pp 11ndash20 2012
[11] C O Akinbile and M S Yusoff ldquoGrowth yield and water usepattern of chilli pepper under different irrigation schedulingand managementrdquo Asian Journal of Agricultural Research vol5 no 2 pp 154ndash163 2011
[12] Y Huang Y Lan S J Thomson A Fang W C Hoffmann andR E Lacey ldquoDevelopment of soft computing and applicationsin agricultural and biological engineeringrdquo Computers andElectronics in Agriculture vol 71 no 2 pp 107ndash127 2010
[13] M Omid M Lashgari H Mobli R Alimardani S Mohtasebiand R Hesamifard ldquoDesign of fuzzy logic control systemincorporating human expert knowledge for combine harvesterrdquoExpert Systems with Applications vol 37 no 10 pp 7080ndash70852010
[14] A Merot and J-E Bergez ldquoIRRIGATE a dynamic integratedmodel combining a knowledge-based model and mechanisticbiophysical models for border irrigation managementrdquo Envi-ronmental Modelling and Software vol 25 no 4 pp 421ndash4322010
[15] S L Davis and M D Dukes ldquoIrrigation scheduling perfor-mance by evapotranspiration-based controllersrdquo AgriculturalWater Management vol 98 no 1 pp 19ndash28 2010
[16] Z Yao G Lou Z XiuLi and Q Zhao ldquoResearch and devel-opment precision irrigation control system in agriculturalrdquoin Proceedings of the International Conference on Computerand Communication Technologies in Agriculture Engineering(CCTAE rsquo10) pp 117ndash120 June 2010
[17] E Garcia Modificaciones al Sistema de Clasificacion Climaticode Koppen Serie de Libros no 6 UNAM Instituto deGeografıaMexico City Mexico 5th edition 2004
International Journal of Distributed Sensor Networks 13
[18] K M Passino S Yurkovich and M Reinfrank Fuzzy Controlvol 42 Addison Wesley Longman Menlo Park Calif USA1998
[19] A Perez-Gutierrez A Pineda-Doporto L Latournerie-Moreno and C Godoy-Avila ldquoNiveles de evapotranspiracionpotencial en la produccion de chile habanerordquo Terra Latino-americana vol 26 no 1 pp 53ndash59 2008
[20] W C Q Ortiz A Perez-Gutierrez L L Moreno C May-LaraE R Sanchez and A J M Chacon ldquoUso de agua potencialhıdrico y rendimiento de chile habanero (Capsicum chinenseJacq)rdquo Revista Fitotecnia Mexicana vol 35 no 2 pp 155ndash1602012
International Journal of Distributed Sensor Networks 7
Shaded houseinfrastructure
Sensors
Sensors
SensorsSensors
SensorsSensors
External section
ZigBee
ZigBeeZigBee
coordinator
end device
ZigBeeend device
Internal section
router
GPRS Ethernet WiFi
Centralprocessing
Arduino + Xbee
Arduino + Xbee
50m
Figure 9 Internal and external sections
65432100 10 20 30 40 50 60 70
Days after transplant
Etc (
mm
)
Figure 10 Evapotranspiration values for the crop cycle
the results of calculations performed for the water loss byevapotranspiration reference (Eto) as shown in the work ofPerez-Gutierrez et al [19] The comparison between fuzzysystems of irrigation and traditional irrigationwas performedto validate the data obtained
The Etc shows values that are in an interval of 3mmsdotdayminus1(Figure 10) This range is stable for all the harvest period ifvariations of the Etc were more spacious and then shouldtake into account a greater number of variables thereforeby maintaining a controlled environment with the use of theshadow house we can use a small number of sensors and inthis proposal the use of three of them is enough to estimatethe irrigation
The system showed a deviation of +011 in Marchminus039 in April and +051 in May (Figures 11 12 and13) The supplied volume by the fuzzy system is comparedwith the calculated volume resulting in a small deviationtherefore we can establish the reliability of the system
0500
10001500200025003000350040004500
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
Volu
me (
L)
Days (March)
Calculated volume (L)Supplied volume (L)
Figure 11 Comparison between traditional irrigation and the fuzzysystem (March 2013)
With the fuzzy system validated the analysis of the datafor eachmonth can be made As part of the validation sampleMarch 20 is shown In Figure 14 the frequency of irrigationduring the day is graphed
According to the data obtained with respect to thefrequency of irrigation Figure 14 shows that the frequencyof irrigation is higher in the time period between 1030and 1300 in this period we can observe an increase trendin the temperature (Figure 15) and a low relative humidity(Figure 16) indicating a water accumulation in the soiltherefore a balance between evapotranspiration and thewater applied to the crop is generated
8 International Journal of Distributed Sensor Networks
Table4(a)D
atafrom
them
onth
ofMarch
forthe
cycle
ofcrop(b)
Datafrom
them
onth
ofAp
rilforthe
cycle
ofcrop(c)Datafrom
them
onth
ofMay
forthe
cycle
ofcrop
(a)
March
Day
Etc(mmsdotdıaminus1 )
FuzzyIrrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
Day
Etc
(mmsdotdıaminus1)
Fuzzy
Irrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy
(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
2258425473
1341340
92235568182
2029668918
17357783512
168119
182801986364
2810
025131
3257725364
10913627
1818
937879
2024170272
18307273124
14404782
240079697
2413
317471
437044
9566
15902236
2650372727
2909504
086
19373218858
17796109
2966018182
2931254056
5384566297
182036
3033933333
3020376637
2039600
9363
18436127
3072
687879
3110
250267
6333154632
149705
2495
083333
2616
59036
2134154618
15833791
2638965152
2682497426
7307303393
14569518
2472
201515
2413
555204
22444
59937
22096245
3682707576
3491
875286
837064
8946
17890227
2981704545
2911070015
23450743937
2116
9736
3528289394
3540134599
93627264
2817962364
2993
727273
2848846
704
24467591342
21811636
3635272727
3672
453809
10383003911
1852
4945
3174
921212
3008105683
2513
804632
566
89909
9448318182
1084213265
112655606
8411413364
1902227273
2085708733
26096752606
45595818
759930303
7598931903
1224746
8837
11749273
1999
775758
1943615697
27366104898
17867809
2977
968182
287538114
113
231084615
1112
2518
185375303
1814
934321
28346
753609
1576
0455
2626742424
2723396
476
14242255034
10806
673
1801112121
1902666591
29460879854
22293755
3715
625758
3619
741905
15349001336
1674
8164
2791
3606
062741050084
30462318448
21561836
3593
639394
3631040
596
16369959684
17351655
2891
942424
2905656565
31488327963
22941936
3823656061
3835318856
(b)
April
Day
Etc(mmsdotdıaminus1)
FuzzyIrrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
Day
Etc
(mmsdotdıaminus1)
FuzzyIrrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
13961718653
18771482
3128580303
3111526554
16399938767
17371336
2895
222727
31411117
32
4169559209
25227564
4204593939
3274
764145
17409686903
192428
3207133333
3217
673412
3404
8530602
18909436
3151572727
3179
708499
18471907325
21807836
3634639394
370635146
44
3418113126
15290055
2548342424
2684579772
195057651274
22931773
3821962121
3972
270022
54344343997
20729182
3454863636
3412
039796
203687610895
14040
036
23400
06061
2896
242824
652164
59781
24479782
4079
963636
4096
997931
214583233577
21846
064
364101060
63599
663234
75021289634
23754855
3959142424
3943711656
223708343758
17238536
2873
089394
2912
526377
84818401971
25410745
4235124242
3784364
058
235197175777
24660
44110
0666
6740
8185231
94929600332
2337
8527
3896
421212
3871699047
245013674745
23637727
3939621212
3937730937
104425304
818
2076
9664
346161060
63475
626276
25460
4622494
2139
2809
3565468182
3616
46205
1143046
8744
720112073
3352012121
3380893615
264715697984
22946236
3824372727
3703700535
12303026964
214473727
2412
287879
2379
968211
274767017231
2235
9845
37266
40909
37440
06578
International Journal of Distributed Sensor Networks 9
(b)Con
tinued
April
Day
Etc(mmsdotdıaminus1)
FuzzyIrrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
Day
Etc
(mmsdotdıaminus1)
FuzzyIrrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
134228506725
1981396
43302327273
3321061416
2849999503
2313
4864
385581060
63926951783
14419946964
121049973
3508328788
3298
255743
294685183652
2216
5509
3694
251515
3679
734635
1541340
0889
18688127
3114
687879
3246842989
303708190798
17870818
2978
469697
2912
406242
(c)
May
Day
Etc(mmsdotdıaminus1)
FuzzyIrrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
Day
Etc
(mmsdotdıaminus1)
Fuzzy
Irrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy
(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
134840
0241
16046282
2674
3803
273632909
14281323798
12056146
200935758
22095119
42
428945109
19954027
332567121
336892701
15345795001
15992027
266533788
2715
86759
3364
868417
17514855
2919
14242
286566985
16376349121
17962409
2993
73485
295583908
4400339841
190312
317186667
314426176
17341760316
1610
1982
268366364
268417925
527064
0388
1241364
62068940
912125604
6418
343767282
15971582
26619303
2699
94192
6351451778
16295346
2715
89091
276029581
19313612887
15119536
2519
92273
243822456
7329792176
1609100
9268183485
2590
1817
20293326251
14367546
2394
59091
246310986
8335111737
15496273
258271212
263196142
21305215788
13911227
2318
53788
230377899
9358129742
16387591
273126515
2812
7444
222
304
887854
14010273
233504545
2397
15919
10365825019
1766
4609
294410152
287318298
23252884747
1503564
62505940
912394
58361
11377619727
17437236
2906206
0629658184
24252884747
113733
1895
55198615216
12373897867
17908982
29848303
293658698
25320855932
1554804
62591
34091
2519
99659
13283378267
1312
6791
218779848
222564771
10 International Journal of Distributed Sensor Networks
Calculated volume (L)Supplied volume (L)
0500
100015002000250030003500400045005000
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Volu
me (
L)
Days (April)
Figure 12 Comparison between traditional irrigation and the fuzzysystem (April 2013)
Calculated volume (L)Supplied volume (L)
0500
1000150020002500300035004000
1 3 5 7 9 11 13 15 17 19 21 23 25
Volu
me (
L)
Days (May)
Figure 13 Comparison between traditional irrigation and the fuzzysystem (May 2013)
These results establish the reliability of the proposedsystem to provide adequate water and maintain optimummoisture level with low relative humidity to obtain thedesirable quality of hbanero pepper production
In thework ofOrtiz et al [20] the percentage of optimumsoil moisture for growing good quality fruits is 60 the fuzzysystem employs the percentage of 70 indicated by the expertso that it is possible to also set the reliability in the finalquality of the fruit The work presented by Perez-Gutierrezet al [19] mentioned that the volume of water applied to80 of potential evapotranspiration generates the amount ofwater in the soil to favor a constant process of transpirationfruit yield and improvement of water use this requires awater volume 2223m3 haminus1 however the fuzzy system onlyrequires 44995m3 haminus1 which represents a saving of 798water
The proposed system is a better form to schedule irriga-tion unlike Yao et al [16] the objective is to give to the poorcommunities a form to harvest a high demand product likehabanero pepper (with designation of origin) with enoughtechnologic structure The work of Yao et al [16] is using aneural network to refine the sprinkle time based on a patternthat is not described in the paper in a real environmentespecially in Yucatan Mexico the humidity and temperaturevariables have to be considered to obtain a quality product
02468
101214161820
000 224 448 712 936 1200 1424 1648 1912
Dur
atio
n of
irrig
atio
n (m
in)
Hours of day
Figure 14 Irrigation frequency using the fuzzy system (sample of20 March 2013)
40
35
30
25
20
15
10
5
0600 824 1048 1312 1536 1800
Hours of day
Tem
pera
ture
(∘C)
Figure 15 Temperature measurement (sample of 20 March 2013)
1009080706050403020100600 824 1048
Hours of day1312 1536 1800
Tem
pera
ture
(∘C)
Figure 16 Measurement of relative humidity (sample of 20 March2013)
and unlike the work of Yao et al [16] in this work are takeninto account in the fuzzy model and in the expert knowledgeThe use of a neural network is not a significant contributionfor managing the time of irrigation on a shadow housebecause there are no other important variables included andin the Yao et al [16] proposal only the soil moisture and theerror are considered to set the irrigation time
4 Conclusions
This paper has presented a fuzzy system that manages awireless irrigation scheme through an algorithm that takesinto account the conditions of microclimate of a shadowhouse used for growing habanero pepper with designation oforigin in the state of Yucatan The water volume consumed
International Journal of Distributed Sensor Networks 11
float h = dhtreadHumidity( )
float t = dhtreadTemperature()
revisa si retorna un valor valido de lo contrario hay un error
if (isnan (t) || isnan (h)) (
Serialprintln (Failed to read from DHT)
) else (
String hume = Humedad
Serialprintln (hume + h + de Humedad)
String tempe = Temperatura
Serialprintln (tempe + t + Grados Centrigrados )
)
Algorithm 2 Section of the Arduino code
SerialPort conectar = new SerialPort (COM4 9600)
conectarOpen( )
while ( shouldStop)
if (conectarBytesToRead gt 0)
string t = conectarReadLine( )
string h = conectarReadLine( )
string m = conectarReadLine( )
string texto
t = tReplace(r )
resp textText = t
hume textText = h
mov textText = m
if (tStartWith(Temperatura ))
Texto = t
t = tReplace(Temperatura )
else resp textText =
if (hStartWith(Humedad ))
texto = h
h = hReplace(Humedad )
else hume textText =
Algorithm 3 Application connection
according to the graphic that shows the comparison betweenthe volume of water from the system and the manual ortraditional volume gives us a difference of 798 over the bestresult shown in Perez-Gutierrez et al [19] which shows thatthe system can efficiently manage water and that incorpora-tion of the expert knowledge in fuzzy rules allows irrigationscheme without using the pan evaporation or calculating thevolume associated with irrigation as they can be automat-ically supplied without the crop being adversely affected inits development Saving water is about 1524823m3 for a cropof 1000 plants in an 85-day cycle causing transplantation atthe 30th day The microclimate conditions generated by theinfrastructure of shadow house are not exploited by the lack
of modernization that is a producer who does not have theability to use technology to irrigate their crops generally onlyconsidered during the morning or afternoon which doesnot guarantee the product quality since plants could fall ineither water stress or excess moisture However the proposedsystem allows for irrigation supply data from three commonsensors (temperature relative humidity and soil moisture)allowing similarly for reducing infrastructure costs
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
12 International Journal of Distributed Sensor Networks
public void bd tempe( )
SqlConnection con = new SqlConnection()
conConnectionString = Data Source=DAVIDSERVERSQLEXPRESS
Initial Catalog=ARDUINOIntegrated Security=True
try
conOpen( )
catch (SqlException ex)
MessageBoxShow(exMessage)
throw
string tiempo = DateTimeNowToString( )
SqlCommand instruccion = conCreateCommand( )
instruccionCommandText = insert into tempe(horatemperatura)
The authors gratefully acknowledge the support of the Tech-nological Institute of Conkal ofMexico and Prometeo Projectof the Secretariat for Higher Education Science Technologyand Innovation of the Republic of Ecuador and CYTEDnetwork 514RT0486
References
[1] FAOSTAT FAO Statistical Databases amp Data-Sets Food andAgriculture Organization of the United Nations 2012 (Consul-tado Dic 2013) httpfaostatfaoorgsite567DesktopDefaultaspxPageID=567ancor
[2] SIAP Agri-Food and Fishing Information and Statistics ServiceSAGARPA Anuario estadıstico de la Produccion AgrıcolaMexico City Mexico 2011
[3] USDA and FAS ldquoGreenhouse and shade house production tocontinue increasingrdquo GAIN Report MX0024 vol 22 USDAFAS Mexico DF Mexico 2010
[4] H Turral M Svendsen and J M Faures ldquoInvesting in irriga-tion reviewing the past and looking to the futurerdquo AgriculturalWater Management vol 97 no 4 pp 551ndash560 2010
[5] S Tang Q Zhu X Zhou S Liu and M Wu ldquoA conceptionof digital agriculturerdquo in Proceedings of the IEEE InternationalGeoscience and Remote Sensing Symposium (IGARSS rsquo02) vol5 pp 3026ndash3028 June 2002
[6] L Bacci P Battista and B Rapi ldquoAn integrated method forirrigation scheduling of potted plantsrdquo Scientia Horticulturaevol 116 no 1 pp 89ndash97 2008
[7] R Lopez Lopez R Arteaga Ramırez M A Vazquez Pena ILopez Cruz and I Sanchez Cohen ldquoIndice de estres hıdricocomo un indicador del momento de riego en cultivos agrıcolasrdquoAgricultura Tecnica en Mexico vol 35 no 1 pp 97ndash111 2009
[8] N Livellara F Saavedra and E Salgado ldquoPlant based indicatorsfor irrigation scheduling in young cherry treesrdquo AgriculturalWater Management vol 98 no 4 pp 684ndash690 2011
[9] R Qiu S Kang F Li et al ldquoEnergy partitioning and evapotran-spiration of hot pepper grown in greenhouse with furrow anddrip irrigation methodsrdquo Scientia Horticulturae vol 129 no 4pp 790ndash797 2011
[10] J Casadesus M Mata J Marsal and J Girona ldquoA generalalgorithm for automated scheduling of drip irrigation in treecropsrdquo Computers and Electronics in Agriculture vol 83 pp 11ndash20 2012
[11] C O Akinbile and M S Yusoff ldquoGrowth yield and water usepattern of chilli pepper under different irrigation schedulingand managementrdquo Asian Journal of Agricultural Research vol5 no 2 pp 154ndash163 2011
[12] Y Huang Y Lan S J Thomson A Fang W C Hoffmann andR E Lacey ldquoDevelopment of soft computing and applicationsin agricultural and biological engineeringrdquo Computers andElectronics in Agriculture vol 71 no 2 pp 107ndash127 2010
[13] M Omid M Lashgari H Mobli R Alimardani S Mohtasebiand R Hesamifard ldquoDesign of fuzzy logic control systemincorporating human expert knowledge for combine harvesterrdquoExpert Systems with Applications vol 37 no 10 pp 7080ndash70852010
[14] A Merot and J-E Bergez ldquoIRRIGATE a dynamic integratedmodel combining a knowledge-based model and mechanisticbiophysical models for border irrigation managementrdquo Envi-ronmental Modelling and Software vol 25 no 4 pp 421ndash4322010
[15] S L Davis and M D Dukes ldquoIrrigation scheduling perfor-mance by evapotranspiration-based controllersrdquo AgriculturalWater Management vol 98 no 1 pp 19ndash28 2010
[16] Z Yao G Lou Z XiuLi and Q Zhao ldquoResearch and devel-opment precision irrigation control system in agriculturalrdquoin Proceedings of the International Conference on Computerand Communication Technologies in Agriculture Engineering(CCTAE rsquo10) pp 117ndash120 June 2010
[17] E Garcia Modificaciones al Sistema de Clasificacion Climaticode Koppen Serie de Libros no 6 UNAM Instituto deGeografıaMexico City Mexico 5th edition 2004
International Journal of Distributed Sensor Networks 13
[18] K M Passino S Yurkovich and M Reinfrank Fuzzy Controlvol 42 Addison Wesley Longman Menlo Park Calif USA1998
[19] A Perez-Gutierrez A Pineda-Doporto L Latournerie-Moreno and C Godoy-Avila ldquoNiveles de evapotranspiracionpotencial en la produccion de chile habanerordquo Terra Latino-americana vol 26 no 1 pp 53ndash59 2008
[20] W C Q Ortiz A Perez-Gutierrez L L Moreno C May-LaraE R Sanchez and A J M Chacon ldquoUso de agua potencialhıdrico y rendimiento de chile habanero (Capsicum chinenseJacq)rdquo Revista Fitotecnia Mexicana vol 35 no 2 pp 155ndash1602012
8 International Journal of Distributed Sensor Networks
Table4(a)D
atafrom
them
onth
ofMarch
forthe
cycle
ofcrop(b)
Datafrom
them
onth
ofAp
rilforthe
cycle
ofcrop(c)Datafrom
them
onth
ofMay
forthe
cycle
ofcrop
(a)
March
Day
Etc(mmsdotdıaminus1 )
FuzzyIrrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
Day
Etc
(mmsdotdıaminus1)
Fuzzy
Irrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy
(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
2258425473
1341340
92235568182
2029668918
17357783512
168119
182801986364
2810
025131
3257725364
10913627
1818
937879
2024170272
18307273124
14404782
240079697
2413
317471
437044
9566
15902236
2650372727
2909504
086
19373218858
17796109
2966018182
2931254056
5384566297
182036
3033933333
3020376637
2039600
9363
18436127
3072
687879
3110
250267
6333154632
149705
2495
083333
2616
59036
2134154618
15833791
2638965152
2682497426
7307303393
14569518
2472
201515
2413
555204
22444
59937
22096245
3682707576
3491
875286
837064
8946
17890227
2981704545
2911070015
23450743937
2116
9736
3528289394
3540134599
93627264
2817962364
2993
727273
2848846
704
24467591342
21811636
3635272727
3672
453809
10383003911
1852
4945
3174
921212
3008105683
2513
804632
566
89909
9448318182
1084213265
112655606
8411413364
1902227273
2085708733
26096752606
45595818
759930303
7598931903
1224746
8837
11749273
1999
775758
1943615697
27366104898
17867809
2977
968182
287538114
113
231084615
1112
2518
185375303
1814
934321
28346
753609
1576
0455
2626742424
2723396
476
14242255034
10806
673
1801112121
1902666591
29460879854
22293755
3715
625758
3619
741905
15349001336
1674
8164
2791
3606
062741050084
30462318448
21561836
3593
639394
3631040
596
16369959684
17351655
2891
942424
2905656565
31488327963
22941936
3823656061
3835318856
(b)
April
Day
Etc(mmsdotdıaminus1)
FuzzyIrrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
Day
Etc
(mmsdotdıaminus1)
FuzzyIrrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
13961718653
18771482
3128580303
3111526554
16399938767
17371336
2895
222727
31411117
32
4169559209
25227564
4204593939
3274
764145
17409686903
192428
3207133333
3217
673412
3404
8530602
18909436
3151572727
3179
708499
18471907325
21807836
3634639394
370635146
44
3418113126
15290055
2548342424
2684579772
195057651274
22931773
3821962121
3972
270022
54344343997
20729182
3454863636
3412
039796
203687610895
14040
036
23400
06061
2896
242824
652164
59781
24479782
4079
963636
4096
997931
214583233577
21846
064
364101060
63599
663234
75021289634
23754855
3959142424
3943711656
223708343758
17238536
2873
089394
2912
526377
84818401971
25410745
4235124242
3784364
058
235197175777
24660
44110
0666
6740
8185231
94929600332
2337
8527
3896
421212
3871699047
245013674745
23637727
3939621212
3937730937
104425304
818
2076
9664
346161060
63475
626276
25460
4622494
2139
2809
3565468182
3616
46205
1143046
8744
720112073
3352012121
3380893615
264715697984
22946236
3824372727
3703700535
12303026964
214473727
2412
287879
2379
968211
274767017231
2235
9845
37266
40909
37440
06578
International Journal of Distributed Sensor Networks 9
(b)Con
tinued
April
Day
Etc(mmsdotdıaminus1)
FuzzyIrrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
Day
Etc
(mmsdotdıaminus1)
FuzzyIrrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
134228506725
1981396
43302327273
3321061416
2849999503
2313
4864
385581060
63926951783
14419946964
121049973
3508328788
3298
255743
294685183652
2216
5509
3694
251515
3679
734635
1541340
0889
18688127
3114
687879
3246842989
303708190798
17870818
2978
469697
2912
406242
(c)
May
Day
Etc(mmsdotdıaminus1)
FuzzyIrrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
Day
Etc
(mmsdotdıaminus1)
Fuzzy
Irrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy
(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
134840
0241
16046282
2674
3803
273632909
14281323798
12056146
200935758
22095119
42
428945109
19954027
332567121
336892701
15345795001
15992027
266533788
2715
86759
3364
868417
17514855
2919
14242
286566985
16376349121
17962409
2993
73485
295583908
4400339841
190312
317186667
314426176
17341760316
1610
1982
268366364
268417925
527064
0388
1241364
62068940
912125604
6418
343767282
15971582
26619303
2699
94192
6351451778
16295346
2715
89091
276029581
19313612887
15119536
2519
92273
243822456
7329792176
1609100
9268183485
2590
1817
20293326251
14367546
2394
59091
246310986
8335111737
15496273
258271212
263196142
21305215788
13911227
2318
53788
230377899
9358129742
16387591
273126515
2812
7444
222
304
887854
14010273
233504545
2397
15919
10365825019
1766
4609
294410152
287318298
23252884747
1503564
62505940
912394
58361
11377619727
17437236
2906206
0629658184
24252884747
113733
1895
55198615216
12373897867
17908982
29848303
293658698
25320855932
1554804
62591
34091
2519
99659
13283378267
1312
6791
218779848
222564771
10 International Journal of Distributed Sensor Networks
Calculated volume (L)Supplied volume (L)
0500
100015002000250030003500400045005000
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Volu
me (
L)
Days (April)
Figure 12 Comparison between traditional irrigation and the fuzzysystem (April 2013)
Calculated volume (L)Supplied volume (L)
0500
1000150020002500300035004000
1 3 5 7 9 11 13 15 17 19 21 23 25
Volu
me (
L)
Days (May)
Figure 13 Comparison between traditional irrigation and the fuzzysystem (May 2013)
These results establish the reliability of the proposedsystem to provide adequate water and maintain optimummoisture level with low relative humidity to obtain thedesirable quality of hbanero pepper production
In thework ofOrtiz et al [20] the percentage of optimumsoil moisture for growing good quality fruits is 60 the fuzzysystem employs the percentage of 70 indicated by the expertso that it is possible to also set the reliability in the finalquality of the fruit The work presented by Perez-Gutierrezet al [19] mentioned that the volume of water applied to80 of potential evapotranspiration generates the amount ofwater in the soil to favor a constant process of transpirationfruit yield and improvement of water use this requires awater volume 2223m3 haminus1 however the fuzzy system onlyrequires 44995m3 haminus1 which represents a saving of 798water
The proposed system is a better form to schedule irriga-tion unlike Yao et al [16] the objective is to give to the poorcommunities a form to harvest a high demand product likehabanero pepper (with designation of origin) with enoughtechnologic structure The work of Yao et al [16] is using aneural network to refine the sprinkle time based on a patternthat is not described in the paper in a real environmentespecially in Yucatan Mexico the humidity and temperaturevariables have to be considered to obtain a quality product
02468
101214161820
000 224 448 712 936 1200 1424 1648 1912
Dur
atio
n of
irrig
atio
n (m
in)
Hours of day
Figure 14 Irrigation frequency using the fuzzy system (sample of20 March 2013)
40
35
30
25
20
15
10
5
0600 824 1048 1312 1536 1800
Hours of day
Tem
pera
ture
(∘C)
Figure 15 Temperature measurement (sample of 20 March 2013)
1009080706050403020100600 824 1048
Hours of day1312 1536 1800
Tem
pera
ture
(∘C)
Figure 16 Measurement of relative humidity (sample of 20 March2013)
and unlike the work of Yao et al [16] in this work are takeninto account in the fuzzy model and in the expert knowledgeThe use of a neural network is not a significant contributionfor managing the time of irrigation on a shadow housebecause there are no other important variables included andin the Yao et al [16] proposal only the soil moisture and theerror are considered to set the irrigation time
4 Conclusions
This paper has presented a fuzzy system that manages awireless irrigation scheme through an algorithm that takesinto account the conditions of microclimate of a shadowhouse used for growing habanero pepper with designation oforigin in the state of Yucatan The water volume consumed
International Journal of Distributed Sensor Networks 11
float h = dhtreadHumidity( )
float t = dhtreadTemperature()
revisa si retorna un valor valido de lo contrario hay un error
if (isnan (t) || isnan (h)) (
Serialprintln (Failed to read from DHT)
) else (
String hume = Humedad
Serialprintln (hume + h + de Humedad)
String tempe = Temperatura
Serialprintln (tempe + t + Grados Centrigrados )
)
Algorithm 2 Section of the Arduino code
SerialPort conectar = new SerialPort (COM4 9600)
conectarOpen( )
while ( shouldStop)
if (conectarBytesToRead gt 0)
string t = conectarReadLine( )
string h = conectarReadLine( )
string m = conectarReadLine( )
string texto
t = tReplace(r )
resp textText = t
hume textText = h
mov textText = m
if (tStartWith(Temperatura ))
Texto = t
t = tReplace(Temperatura )
else resp textText =
if (hStartWith(Humedad ))
texto = h
h = hReplace(Humedad )
else hume textText =
Algorithm 3 Application connection
according to the graphic that shows the comparison betweenthe volume of water from the system and the manual ortraditional volume gives us a difference of 798 over the bestresult shown in Perez-Gutierrez et al [19] which shows thatthe system can efficiently manage water and that incorpora-tion of the expert knowledge in fuzzy rules allows irrigationscheme without using the pan evaporation or calculating thevolume associated with irrigation as they can be automat-ically supplied without the crop being adversely affected inits development Saving water is about 1524823m3 for a cropof 1000 plants in an 85-day cycle causing transplantation atthe 30th day The microclimate conditions generated by theinfrastructure of shadow house are not exploited by the lack
of modernization that is a producer who does not have theability to use technology to irrigate their crops generally onlyconsidered during the morning or afternoon which doesnot guarantee the product quality since plants could fall ineither water stress or excess moisture However the proposedsystem allows for irrigation supply data from three commonsensors (temperature relative humidity and soil moisture)allowing similarly for reducing infrastructure costs
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
12 International Journal of Distributed Sensor Networks
public void bd tempe( )
SqlConnection con = new SqlConnection()
conConnectionString = Data Source=DAVIDSERVERSQLEXPRESS
Initial Catalog=ARDUINOIntegrated Security=True
try
conOpen( )
catch (SqlException ex)
MessageBoxShow(exMessage)
throw
string tiempo = DateTimeNowToString( )
SqlCommand instruccion = conCreateCommand( )
instruccionCommandText = insert into tempe(horatemperatura)
The authors gratefully acknowledge the support of the Tech-nological Institute of Conkal ofMexico and Prometeo Projectof the Secretariat for Higher Education Science Technologyand Innovation of the Republic of Ecuador and CYTEDnetwork 514RT0486
References
[1] FAOSTAT FAO Statistical Databases amp Data-Sets Food andAgriculture Organization of the United Nations 2012 (Consul-tado Dic 2013) httpfaostatfaoorgsite567DesktopDefaultaspxPageID=567ancor
[2] SIAP Agri-Food and Fishing Information and Statistics ServiceSAGARPA Anuario estadıstico de la Produccion AgrıcolaMexico City Mexico 2011
[3] USDA and FAS ldquoGreenhouse and shade house production tocontinue increasingrdquo GAIN Report MX0024 vol 22 USDAFAS Mexico DF Mexico 2010
[4] H Turral M Svendsen and J M Faures ldquoInvesting in irriga-tion reviewing the past and looking to the futurerdquo AgriculturalWater Management vol 97 no 4 pp 551ndash560 2010
[5] S Tang Q Zhu X Zhou S Liu and M Wu ldquoA conceptionof digital agriculturerdquo in Proceedings of the IEEE InternationalGeoscience and Remote Sensing Symposium (IGARSS rsquo02) vol5 pp 3026ndash3028 June 2002
[6] L Bacci P Battista and B Rapi ldquoAn integrated method forirrigation scheduling of potted plantsrdquo Scientia Horticulturaevol 116 no 1 pp 89ndash97 2008
[7] R Lopez Lopez R Arteaga Ramırez M A Vazquez Pena ILopez Cruz and I Sanchez Cohen ldquoIndice de estres hıdricocomo un indicador del momento de riego en cultivos agrıcolasrdquoAgricultura Tecnica en Mexico vol 35 no 1 pp 97ndash111 2009
[8] N Livellara F Saavedra and E Salgado ldquoPlant based indicatorsfor irrigation scheduling in young cherry treesrdquo AgriculturalWater Management vol 98 no 4 pp 684ndash690 2011
[9] R Qiu S Kang F Li et al ldquoEnergy partitioning and evapotran-spiration of hot pepper grown in greenhouse with furrow anddrip irrigation methodsrdquo Scientia Horticulturae vol 129 no 4pp 790ndash797 2011
[10] J Casadesus M Mata J Marsal and J Girona ldquoA generalalgorithm for automated scheduling of drip irrigation in treecropsrdquo Computers and Electronics in Agriculture vol 83 pp 11ndash20 2012
[11] C O Akinbile and M S Yusoff ldquoGrowth yield and water usepattern of chilli pepper under different irrigation schedulingand managementrdquo Asian Journal of Agricultural Research vol5 no 2 pp 154ndash163 2011
[12] Y Huang Y Lan S J Thomson A Fang W C Hoffmann andR E Lacey ldquoDevelopment of soft computing and applicationsin agricultural and biological engineeringrdquo Computers andElectronics in Agriculture vol 71 no 2 pp 107ndash127 2010
[13] M Omid M Lashgari H Mobli R Alimardani S Mohtasebiand R Hesamifard ldquoDesign of fuzzy logic control systemincorporating human expert knowledge for combine harvesterrdquoExpert Systems with Applications vol 37 no 10 pp 7080ndash70852010
[14] A Merot and J-E Bergez ldquoIRRIGATE a dynamic integratedmodel combining a knowledge-based model and mechanisticbiophysical models for border irrigation managementrdquo Envi-ronmental Modelling and Software vol 25 no 4 pp 421ndash4322010
[15] S L Davis and M D Dukes ldquoIrrigation scheduling perfor-mance by evapotranspiration-based controllersrdquo AgriculturalWater Management vol 98 no 1 pp 19ndash28 2010
[16] Z Yao G Lou Z XiuLi and Q Zhao ldquoResearch and devel-opment precision irrigation control system in agriculturalrdquoin Proceedings of the International Conference on Computerand Communication Technologies in Agriculture Engineering(CCTAE rsquo10) pp 117ndash120 June 2010
[17] E Garcia Modificaciones al Sistema de Clasificacion Climaticode Koppen Serie de Libros no 6 UNAM Instituto deGeografıaMexico City Mexico 5th edition 2004
International Journal of Distributed Sensor Networks 13
[18] K M Passino S Yurkovich and M Reinfrank Fuzzy Controlvol 42 Addison Wesley Longman Menlo Park Calif USA1998
[19] A Perez-Gutierrez A Pineda-Doporto L Latournerie-Moreno and C Godoy-Avila ldquoNiveles de evapotranspiracionpotencial en la produccion de chile habanerordquo Terra Latino-americana vol 26 no 1 pp 53ndash59 2008
[20] W C Q Ortiz A Perez-Gutierrez L L Moreno C May-LaraE R Sanchez and A J M Chacon ldquoUso de agua potencialhıdrico y rendimiento de chile habanero (Capsicum chinenseJacq)rdquo Revista Fitotecnia Mexicana vol 35 no 2 pp 155ndash1602012
International Journal of Distributed Sensor Networks 9
(b)Con
tinued
April
Day
Etc(mmsdotdıaminus1)
FuzzyIrrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
Day
Etc
(mmsdotdıaminus1)
FuzzyIrrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
134228506725
1981396
43302327273
3321061416
2849999503
2313
4864
385581060
63926951783
14419946964
121049973
3508328788
3298
255743
294685183652
2216
5509
3694
251515
3679
734635
1541340
0889
18688127
3114
687879
3246842989
303708190798
17870818
2978
469697
2912
406242
(c)
May
Day
Etc(mmsdotdıaminus1)
FuzzyIrrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
Day
Etc
(mmsdotdıaminus1)
Fuzzy
Irrig
Time
(minsdotdıaminus1)
Supp
liedV
olFu
zzy
(lsdotdıaminus1)
Supp
liedV
olTrad
(lsdotdıaminus1)
134840
0241
16046282
2674
3803
273632909
14281323798
12056146
200935758
22095119
42
428945109
19954027
332567121
336892701
15345795001
15992027
266533788
2715
86759
3364
868417
17514855
2919
14242
286566985
16376349121
17962409
2993
73485
295583908
4400339841
190312
317186667
314426176
17341760316
1610
1982
268366364
268417925
527064
0388
1241364
62068940
912125604
6418
343767282
15971582
26619303
2699
94192
6351451778
16295346
2715
89091
276029581
19313612887
15119536
2519
92273
243822456
7329792176
1609100
9268183485
2590
1817
20293326251
14367546
2394
59091
246310986
8335111737
15496273
258271212
263196142
21305215788
13911227
2318
53788
230377899
9358129742
16387591
273126515
2812
7444
222
304
887854
14010273
233504545
2397
15919
10365825019
1766
4609
294410152
287318298
23252884747
1503564
62505940
912394
58361
11377619727
17437236
2906206
0629658184
24252884747
113733
1895
55198615216
12373897867
17908982
29848303
293658698
25320855932
1554804
62591
34091
2519
99659
13283378267
1312
6791
218779848
222564771
10 International Journal of Distributed Sensor Networks
Calculated volume (L)Supplied volume (L)
0500
100015002000250030003500400045005000
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Volu
me (
L)
Days (April)
Figure 12 Comparison between traditional irrigation and the fuzzysystem (April 2013)
Calculated volume (L)Supplied volume (L)
0500
1000150020002500300035004000
1 3 5 7 9 11 13 15 17 19 21 23 25
Volu
me (
L)
Days (May)
Figure 13 Comparison between traditional irrigation and the fuzzysystem (May 2013)
These results establish the reliability of the proposedsystem to provide adequate water and maintain optimummoisture level with low relative humidity to obtain thedesirable quality of hbanero pepper production
In thework ofOrtiz et al [20] the percentage of optimumsoil moisture for growing good quality fruits is 60 the fuzzysystem employs the percentage of 70 indicated by the expertso that it is possible to also set the reliability in the finalquality of the fruit The work presented by Perez-Gutierrezet al [19] mentioned that the volume of water applied to80 of potential evapotranspiration generates the amount ofwater in the soil to favor a constant process of transpirationfruit yield and improvement of water use this requires awater volume 2223m3 haminus1 however the fuzzy system onlyrequires 44995m3 haminus1 which represents a saving of 798water
The proposed system is a better form to schedule irriga-tion unlike Yao et al [16] the objective is to give to the poorcommunities a form to harvest a high demand product likehabanero pepper (with designation of origin) with enoughtechnologic structure The work of Yao et al [16] is using aneural network to refine the sprinkle time based on a patternthat is not described in the paper in a real environmentespecially in Yucatan Mexico the humidity and temperaturevariables have to be considered to obtain a quality product
02468
101214161820
000 224 448 712 936 1200 1424 1648 1912
Dur
atio
n of
irrig
atio
n (m
in)
Hours of day
Figure 14 Irrigation frequency using the fuzzy system (sample of20 March 2013)
40
35
30
25
20
15
10
5
0600 824 1048 1312 1536 1800
Hours of day
Tem
pera
ture
(∘C)
Figure 15 Temperature measurement (sample of 20 March 2013)
1009080706050403020100600 824 1048
Hours of day1312 1536 1800
Tem
pera
ture
(∘C)
Figure 16 Measurement of relative humidity (sample of 20 March2013)
and unlike the work of Yao et al [16] in this work are takeninto account in the fuzzy model and in the expert knowledgeThe use of a neural network is not a significant contributionfor managing the time of irrigation on a shadow housebecause there are no other important variables included andin the Yao et al [16] proposal only the soil moisture and theerror are considered to set the irrigation time
4 Conclusions
This paper has presented a fuzzy system that manages awireless irrigation scheme through an algorithm that takesinto account the conditions of microclimate of a shadowhouse used for growing habanero pepper with designation oforigin in the state of Yucatan The water volume consumed
International Journal of Distributed Sensor Networks 11
float h = dhtreadHumidity( )
float t = dhtreadTemperature()
revisa si retorna un valor valido de lo contrario hay un error
if (isnan (t) || isnan (h)) (
Serialprintln (Failed to read from DHT)
) else (
String hume = Humedad
Serialprintln (hume + h + de Humedad)
String tempe = Temperatura
Serialprintln (tempe + t + Grados Centrigrados )
)
Algorithm 2 Section of the Arduino code
SerialPort conectar = new SerialPort (COM4 9600)
conectarOpen( )
while ( shouldStop)
if (conectarBytesToRead gt 0)
string t = conectarReadLine( )
string h = conectarReadLine( )
string m = conectarReadLine( )
string texto
t = tReplace(r )
resp textText = t
hume textText = h
mov textText = m
if (tStartWith(Temperatura ))
Texto = t
t = tReplace(Temperatura )
else resp textText =
if (hStartWith(Humedad ))
texto = h
h = hReplace(Humedad )
else hume textText =
Algorithm 3 Application connection
according to the graphic that shows the comparison betweenthe volume of water from the system and the manual ortraditional volume gives us a difference of 798 over the bestresult shown in Perez-Gutierrez et al [19] which shows thatthe system can efficiently manage water and that incorpora-tion of the expert knowledge in fuzzy rules allows irrigationscheme without using the pan evaporation or calculating thevolume associated with irrigation as they can be automat-ically supplied without the crop being adversely affected inits development Saving water is about 1524823m3 for a cropof 1000 plants in an 85-day cycle causing transplantation atthe 30th day The microclimate conditions generated by theinfrastructure of shadow house are not exploited by the lack
of modernization that is a producer who does not have theability to use technology to irrigate their crops generally onlyconsidered during the morning or afternoon which doesnot guarantee the product quality since plants could fall ineither water stress or excess moisture However the proposedsystem allows for irrigation supply data from three commonsensors (temperature relative humidity and soil moisture)allowing similarly for reducing infrastructure costs
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
12 International Journal of Distributed Sensor Networks
public void bd tempe( )
SqlConnection con = new SqlConnection()
conConnectionString = Data Source=DAVIDSERVERSQLEXPRESS
Initial Catalog=ARDUINOIntegrated Security=True
try
conOpen( )
catch (SqlException ex)
MessageBoxShow(exMessage)
throw
string tiempo = DateTimeNowToString( )
SqlCommand instruccion = conCreateCommand( )
instruccionCommandText = insert into tempe(horatemperatura)
The authors gratefully acknowledge the support of the Tech-nological Institute of Conkal ofMexico and Prometeo Projectof the Secretariat for Higher Education Science Technologyand Innovation of the Republic of Ecuador and CYTEDnetwork 514RT0486
References
[1] FAOSTAT FAO Statistical Databases amp Data-Sets Food andAgriculture Organization of the United Nations 2012 (Consul-tado Dic 2013) httpfaostatfaoorgsite567DesktopDefaultaspxPageID=567ancor
[2] SIAP Agri-Food and Fishing Information and Statistics ServiceSAGARPA Anuario estadıstico de la Produccion AgrıcolaMexico City Mexico 2011
[3] USDA and FAS ldquoGreenhouse and shade house production tocontinue increasingrdquo GAIN Report MX0024 vol 22 USDAFAS Mexico DF Mexico 2010
[4] H Turral M Svendsen and J M Faures ldquoInvesting in irriga-tion reviewing the past and looking to the futurerdquo AgriculturalWater Management vol 97 no 4 pp 551ndash560 2010
[5] S Tang Q Zhu X Zhou S Liu and M Wu ldquoA conceptionof digital agriculturerdquo in Proceedings of the IEEE InternationalGeoscience and Remote Sensing Symposium (IGARSS rsquo02) vol5 pp 3026ndash3028 June 2002
[6] L Bacci P Battista and B Rapi ldquoAn integrated method forirrigation scheduling of potted plantsrdquo Scientia Horticulturaevol 116 no 1 pp 89ndash97 2008
[7] R Lopez Lopez R Arteaga Ramırez M A Vazquez Pena ILopez Cruz and I Sanchez Cohen ldquoIndice de estres hıdricocomo un indicador del momento de riego en cultivos agrıcolasrdquoAgricultura Tecnica en Mexico vol 35 no 1 pp 97ndash111 2009
[8] N Livellara F Saavedra and E Salgado ldquoPlant based indicatorsfor irrigation scheduling in young cherry treesrdquo AgriculturalWater Management vol 98 no 4 pp 684ndash690 2011
[9] R Qiu S Kang F Li et al ldquoEnergy partitioning and evapotran-spiration of hot pepper grown in greenhouse with furrow anddrip irrigation methodsrdquo Scientia Horticulturae vol 129 no 4pp 790ndash797 2011
[10] J Casadesus M Mata J Marsal and J Girona ldquoA generalalgorithm for automated scheduling of drip irrigation in treecropsrdquo Computers and Electronics in Agriculture vol 83 pp 11ndash20 2012
[11] C O Akinbile and M S Yusoff ldquoGrowth yield and water usepattern of chilli pepper under different irrigation schedulingand managementrdquo Asian Journal of Agricultural Research vol5 no 2 pp 154ndash163 2011
[12] Y Huang Y Lan S J Thomson A Fang W C Hoffmann andR E Lacey ldquoDevelopment of soft computing and applicationsin agricultural and biological engineeringrdquo Computers andElectronics in Agriculture vol 71 no 2 pp 107ndash127 2010
[13] M Omid M Lashgari H Mobli R Alimardani S Mohtasebiand R Hesamifard ldquoDesign of fuzzy logic control systemincorporating human expert knowledge for combine harvesterrdquoExpert Systems with Applications vol 37 no 10 pp 7080ndash70852010
[14] A Merot and J-E Bergez ldquoIRRIGATE a dynamic integratedmodel combining a knowledge-based model and mechanisticbiophysical models for border irrigation managementrdquo Envi-ronmental Modelling and Software vol 25 no 4 pp 421ndash4322010
[15] S L Davis and M D Dukes ldquoIrrigation scheduling perfor-mance by evapotranspiration-based controllersrdquo AgriculturalWater Management vol 98 no 1 pp 19ndash28 2010
[16] Z Yao G Lou Z XiuLi and Q Zhao ldquoResearch and devel-opment precision irrigation control system in agriculturalrdquoin Proceedings of the International Conference on Computerand Communication Technologies in Agriculture Engineering(CCTAE rsquo10) pp 117ndash120 June 2010
[17] E Garcia Modificaciones al Sistema de Clasificacion Climaticode Koppen Serie de Libros no 6 UNAM Instituto deGeografıaMexico City Mexico 5th edition 2004
International Journal of Distributed Sensor Networks 13
[18] K M Passino S Yurkovich and M Reinfrank Fuzzy Controlvol 42 Addison Wesley Longman Menlo Park Calif USA1998
[19] A Perez-Gutierrez A Pineda-Doporto L Latournerie-Moreno and C Godoy-Avila ldquoNiveles de evapotranspiracionpotencial en la produccion de chile habanerordquo Terra Latino-americana vol 26 no 1 pp 53ndash59 2008
[20] W C Q Ortiz A Perez-Gutierrez L L Moreno C May-LaraE R Sanchez and A J M Chacon ldquoUso de agua potencialhıdrico y rendimiento de chile habanero (Capsicum chinenseJacq)rdquo Revista Fitotecnia Mexicana vol 35 no 2 pp 155ndash1602012
10 International Journal of Distributed Sensor Networks
Calculated volume (L)Supplied volume (L)
0500
100015002000250030003500400045005000
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Volu
me (
L)
Days (April)
Figure 12 Comparison between traditional irrigation and the fuzzysystem (April 2013)
Calculated volume (L)Supplied volume (L)
0500
1000150020002500300035004000
1 3 5 7 9 11 13 15 17 19 21 23 25
Volu
me (
L)
Days (May)
Figure 13 Comparison between traditional irrigation and the fuzzysystem (May 2013)
These results establish the reliability of the proposedsystem to provide adequate water and maintain optimummoisture level with low relative humidity to obtain thedesirable quality of hbanero pepper production
In thework ofOrtiz et al [20] the percentage of optimumsoil moisture for growing good quality fruits is 60 the fuzzysystem employs the percentage of 70 indicated by the expertso that it is possible to also set the reliability in the finalquality of the fruit The work presented by Perez-Gutierrezet al [19] mentioned that the volume of water applied to80 of potential evapotranspiration generates the amount ofwater in the soil to favor a constant process of transpirationfruit yield and improvement of water use this requires awater volume 2223m3 haminus1 however the fuzzy system onlyrequires 44995m3 haminus1 which represents a saving of 798water
The proposed system is a better form to schedule irriga-tion unlike Yao et al [16] the objective is to give to the poorcommunities a form to harvest a high demand product likehabanero pepper (with designation of origin) with enoughtechnologic structure The work of Yao et al [16] is using aneural network to refine the sprinkle time based on a patternthat is not described in the paper in a real environmentespecially in Yucatan Mexico the humidity and temperaturevariables have to be considered to obtain a quality product
02468
101214161820
000 224 448 712 936 1200 1424 1648 1912
Dur
atio
n of
irrig
atio
n (m
in)
Hours of day
Figure 14 Irrigation frequency using the fuzzy system (sample of20 March 2013)
40
35
30
25
20
15
10
5
0600 824 1048 1312 1536 1800
Hours of day
Tem
pera
ture
(∘C)
Figure 15 Temperature measurement (sample of 20 March 2013)
1009080706050403020100600 824 1048
Hours of day1312 1536 1800
Tem
pera
ture
(∘C)
Figure 16 Measurement of relative humidity (sample of 20 March2013)
and unlike the work of Yao et al [16] in this work are takeninto account in the fuzzy model and in the expert knowledgeThe use of a neural network is not a significant contributionfor managing the time of irrigation on a shadow housebecause there are no other important variables included andin the Yao et al [16] proposal only the soil moisture and theerror are considered to set the irrigation time
4 Conclusions
This paper has presented a fuzzy system that manages awireless irrigation scheme through an algorithm that takesinto account the conditions of microclimate of a shadowhouse used for growing habanero pepper with designation oforigin in the state of Yucatan The water volume consumed
International Journal of Distributed Sensor Networks 11
float h = dhtreadHumidity( )
float t = dhtreadTemperature()
revisa si retorna un valor valido de lo contrario hay un error
if (isnan (t) || isnan (h)) (
Serialprintln (Failed to read from DHT)
) else (
String hume = Humedad
Serialprintln (hume + h + de Humedad)
String tempe = Temperatura
Serialprintln (tempe + t + Grados Centrigrados )
)
Algorithm 2 Section of the Arduino code
SerialPort conectar = new SerialPort (COM4 9600)
conectarOpen( )
while ( shouldStop)
if (conectarBytesToRead gt 0)
string t = conectarReadLine( )
string h = conectarReadLine( )
string m = conectarReadLine( )
string texto
t = tReplace(r )
resp textText = t
hume textText = h
mov textText = m
if (tStartWith(Temperatura ))
Texto = t
t = tReplace(Temperatura )
else resp textText =
if (hStartWith(Humedad ))
texto = h
h = hReplace(Humedad )
else hume textText =
Algorithm 3 Application connection
according to the graphic that shows the comparison betweenthe volume of water from the system and the manual ortraditional volume gives us a difference of 798 over the bestresult shown in Perez-Gutierrez et al [19] which shows thatthe system can efficiently manage water and that incorpora-tion of the expert knowledge in fuzzy rules allows irrigationscheme without using the pan evaporation or calculating thevolume associated with irrigation as they can be automat-ically supplied without the crop being adversely affected inits development Saving water is about 1524823m3 for a cropof 1000 plants in an 85-day cycle causing transplantation atthe 30th day The microclimate conditions generated by theinfrastructure of shadow house are not exploited by the lack
of modernization that is a producer who does not have theability to use technology to irrigate their crops generally onlyconsidered during the morning or afternoon which doesnot guarantee the product quality since plants could fall ineither water stress or excess moisture However the proposedsystem allows for irrigation supply data from three commonsensors (temperature relative humidity and soil moisture)allowing similarly for reducing infrastructure costs
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
12 International Journal of Distributed Sensor Networks
public void bd tempe( )
SqlConnection con = new SqlConnection()
conConnectionString = Data Source=DAVIDSERVERSQLEXPRESS
Initial Catalog=ARDUINOIntegrated Security=True
try
conOpen( )
catch (SqlException ex)
MessageBoxShow(exMessage)
throw
string tiempo = DateTimeNowToString( )
SqlCommand instruccion = conCreateCommand( )
instruccionCommandText = insert into tempe(horatemperatura)
The authors gratefully acknowledge the support of the Tech-nological Institute of Conkal ofMexico and Prometeo Projectof the Secretariat for Higher Education Science Technologyand Innovation of the Republic of Ecuador and CYTEDnetwork 514RT0486
References
[1] FAOSTAT FAO Statistical Databases amp Data-Sets Food andAgriculture Organization of the United Nations 2012 (Consul-tado Dic 2013) httpfaostatfaoorgsite567DesktopDefaultaspxPageID=567ancor
[2] SIAP Agri-Food and Fishing Information and Statistics ServiceSAGARPA Anuario estadıstico de la Produccion AgrıcolaMexico City Mexico 2011
[3] USDA and FAS ldquoGreenhouse and shade house production tocontinue increasingrdquo GAIN Report MX0024 vol 22 USDAFAS Mexico DF Mexico 2010
[4] H Turral M Svendsen and J M Faures ldquoInvesting in irriga-tion reviewing the past and looking to the futurerdquo AgriculturalWater Management vol 97 no 4 pp 551ndash560 2010
[5] S Tang Q Zhu X Zhou S Liu and M Wu ldquoA conceptionof digital agriculturerdquo in Proceedings of the IEEE InternationalGeoscience and Remote Sensing Symposium (IGARSS rsquo02) vol5 pp 3026ndash3028 June 2002
[6] L Bacci P Battista and B Rapi ldquoAn integrated method forirrigation scheduling of potted plantsrdquo Scientia Horticulturaevol 116 no 1 pp 89ndash97 2008
[7] R Lopez Lopez R Arteaga Ramırez M A Vazquez Pena ILopez Cruz and I Sanchez Cohen ldquoIndice de estres hıdricocomo un indicador del momento de riego en cultivos agrıcolasrdquoAgricultura Tecnica en Mexico vol 35 no 1 pp 97ndash111 2009
[8] N Livellara F Saavedra and E Salgado ldquoPlant based indicatorsfor irrigation scheduling in young cherry treesrdquo AgriculturalWater Management vol 98 no 4 pp 684ndash690 2011
[9] R Qiu S Kang F Li et al ldquoEnergy partitioning and evapotran-spiration of hot pepper grown in greenhouse with furrow anddrip irrigation methodsrdquo Scientia Horticulturae vol 129 no 4pp 790ndash797 2011
[10] J Casadesus M Mata J Marsal and J Girona ldquoA generalalgorithm for automated scheduling of drip irrigation in treecropsrdquo Computers and Electronics in Agriculture vol 83 pp 11ndash20 2012
[11] C O Akinbile and M S Yusoff ldquoGrowth yield and water usepattern of chilli pepper under different irrigation schedulingand managementrdquo Asian Journal of Agricultural Research vol5 no 2 pp 154ndash163 2011
[12] Y Huang Y Lan S J Thomson A Fang W C Hoffmann andR E Lacey ldquoDevelopment of soft computing and applicationsin agricultural and biological engineeringrdquo Computers andElectronics in Agriculture vol 71 no 2 pp 107ndash127 2010
[13] M Omid M Lashgari H Mobli R Alimardani S Mohtasebiand R Hesamifard ldquoDesign of fuzzy logic control systemincorporating human expert knowledge for combine harvesterrdquoExpert Systems with Applications vol 37 no 10 pp 7080ndash70852010
[14] A Merot and J-E Bergez ldquoIRRIGATE a dynamic integratedmodel combining a knowledge-based model and mechanisticbiophysical models for border irrigation managementrdquo Envi-ronmental Modelling and Software vol 25 no 4 pp 421ndash4322010
[15] S L Davis and M D Dukes ldquoIrrigation scheduling perfor-mance by evapotranspiration-based controllersrdquo AgriculturalWater Management vol 98 no 1 pp 19ndash28 2010
[16] Z Yao G Lou Z XiuLi and Q Zhao ldquoResearch and devel-opment precision irrigation control system in agriculturalrdquoin Proceedings of the International Conference on Computerand Communication Technologies in Agriculture Engineering(CCTAE rsquo10) pp 117ndash120 June 2010
[17] E Garcia Modificaciones al Sistema de Clasificacion Climaticode Koppen Serie de Libros no 6 UNAM Instituto deGeografıaMexico City Mexico 5th edition 2004
International Journal of Distributed Sensor Networks 13
[18] K M Passino S Yurkovich and M Reinfrank Fuzzy Controlvol 42 Addison Wesley Longman Menlo Park Calif USA1998
[19] A Perez-Gutierrez A Pineda-Doporto L Latournerie-Moreno and C Godoy-Avila ldquoNiveles de evapotranspiracionpotencial en la produccion de chile habanerordquo Terra Latino-americana vol 26 no 1 pp 53ndash59 2008
[20] W C Q Ortiz A Perez-Gutierrez L L Moreno C May-LaraE R Sanchez and A J M Chacon ldquoUso de agua potencialhıdrico y rendimiento de chile habanero (Capsicum chinenseJacq)rdquo Revista Fitotecnia Mexicana vol 35 no 2 pp 155ndash1602012
International Journal of Distributed Sensor Networks 11
float h = dhtreadHumidity( )
float t = dhtreadTemperature()
revisa si retorna un valor valido de lo contrario hay un error
if (isnan (t) || isnan (h)) (
Serialprintln (Failed to read from DHT)
) else (
String hume = Humedad
Serialprintln (hume + h + de Humedad)
String tempe = Temperatura
Serialprintln (tempe + t + Grados Centrigrados )
)
Algorithm 2 Section of the Arduino code
SerialPort conectar = new SerialPort (COM4 9600)
conectarOpen( )
while ( shouldStop)
if (conectarBytesToRead gt 0)
string t = conectarReadLine( )
string h = conectarReadLine( )
string m = conectarReadLine( )
string texto
t = tReplace(r )
resp textText = t
hume textText = h
mov textText = m
if (tStartWith(Temperatura ))
Texto = t
t = tReplace(Temperatura )
else resp textText =
if (hStartWith(Humedad ))
texto = h
h = hReplace(Humedad )
else hume textText =
Algorithm 3 Application connection
according to the graphic that shows the comparison betweenthe volume of water from the system and the manual ortraditional volume gives us a difference of 798 over the bestresult shown in Perez-Gutierrez et al [19] which shows thatthe system can efficiently manage water and that incorpora-tion of the expert knowledge in fuzzy rules allows irrigationscheme without using the pan evaporation or calculating thevolume associated with irrigation as they can be automat-ically supplied without the crop being adversely affected inits development Saving water is about 1524823m3 for a cropof 1000 plants in an 85-day cycle causing transplantation atthe 30th day The microclimate conditions generated by theinfrastructure of shadow house are not exploited by the lack
of modernization that is a producer who does not have theability to use technology to irrigate their crops generally onlyconsidered during the morning or afternoon which doesnot guarantee the product quality since plants could fall ineither water stress or excess moisture However the proposedsystem allows for irrigation supply data from three commonsensors (temperature relative humidity and soil moisture)allowing similarly for reducing infrastructure costs
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
12 International Journal of Distributed Sensor Networks
public void bd tempe( )
SqlConnection con = new SqlConnection()
conConnectionString = Data Source=DAVIDSERVERSQLEXPRESS
Initial Catalog=ARDUINOIntegrated Security=True
try
conOpen( )
catch (SqlException ex)
MessageBoxShow(exMessage)
throw
string tiempo = DateTimeNowToString( )
SqlCommand instruccion = conCreateCommand( )
instruccionCommandText = insert into tempe(horatemperatura)
The authors gratefully acknowledge the support of the Tech-nological Institute of Conkal ofMexico and Prometeo Projectof the Secretariat for Higher Education Science Technologyand Innovation of the Republic of Ecuador and CYTEDnetwork 514RT0486
References
[1] FAOSTAT FAO Statistical Databases amp Data-Sets Food andAgriculture Organization of the United Nations 2012 (Consul-tado Dic 2013) httpfaostatfaoorgsite567DesktopDefaultaspxPageID=567ancor
[2] SIAP Agri-Food and Fishing Information and Statistics ServiceSAGARPA Anuario estadıstico de la Produccion AgrıcolaMexico City Mexico 2011
[3] USDA and FAS ldquoGreenhouse and shade house production tocontinue increasingrdquo GAIN Report MX0024 vol 22 USDAFAS Mexico DF Mexico 2010
[4] H Turral M Svendsen and J M Faures ldquoInvesting in irriga-tion reviewing the past and looking to the futurerdquo AgriculturalWater Management vol 97 no 4 pp 551ndash560 2010
[5] S Tang Q Zhu X Zhou S Liu and M Wu ldquoA conceptionof digital agriculturerdquo in Proceedings of the IEEE InternationalGeoscience and Remote Sensing Symposium (IGARSS rsquo02) vol5 pp 3026ndash3028 June 2002
[6] L Bacci P Battista and B Rapi ldquoAn integrated method forirrigation scheduling of potted plantsrdquo Scientia Horticulturaevol 116 no 1 pp 89ndash97 2008
[7] R Lopez Lopez R Arteaga Ramırez M A Vazquez Pena ILopez Cruz and I Sanchez Cohen ldquoIndice de estres hıdricocomo un indicador del momento de riego en cultivos agrıcolasrdquoAgricultura Tecnica en Mexico vol 35 no 1 pp 97ndash111 2009
[8] N Livellara F Saavedra and E Salgado ldquoPlant based indicatorsfor irrigation scheduling in young cherry treesrdquo AgriculturalWater Management vol 98 no 4 pp 684ndash690 2011
[9] R Qiu S Kang F Li et al ldquoEnergy partitioning and evapotran-spiration of hot pepper grown in greenhouse with furrow anddrip irrigation methodsrdquo Scientia Horticulturae vol 129 no 4pp 790ndash797 2011
[10] J Casadesus M Mata J Marsal and J Girona ldquoA generalalgorithm for automated scheduling of drip irrigation in treecropsrdquo Computers and Electronics in Agriculture vol 83 pp 11ndash20 2012
[11] C O Akinbile and M S Yusoff ldquoGrowth yield and water usepattern of chilli pepper under different irrigation schedulingand managementrdquo Asian Journal of Agricultural Research vol5 no 2 pp 154ndash163 2011
[12] Y Huang Y Lan S J Thomson A Fang W C Hoffmann andR E Lacey ldquoDevelopment of soft computing and applicationsin agricultural and biological engineeringrdquo Computers andElectronics in Agriculture vol 71 no 2 pp 107ndash127 2010
[13] M Omid M Lashgari H Mobli R Alimardani S Mohtasebiand R Hesamifard ldquoDesign of fuzzy logic control systemincorporating human expert knowledge for combine harvesterrdquoExpert Systems with Applications vol 37 no 10 pp 7080ndash70852010
[14] A Merot and J-E Bergez ldquoIRRIGATE a dynamic integratedmodel combining a knowledge-based model and mechanisticbiophysical models for border irrigation managementrdquo Envi-ronmental Modelling and Software vol 25 no 4 pp 421ndash4322010
[15] S L Davis and M D Dukes ldquoIrrigation scheduling perfor-mance by evapotranspiration-based controllersrdquo AgriculturalWater Management vol 98 no 1 pp 19ndash28 2010
[16] Z Yao G Lou Z XiuLi and Q Zhao ldquoResearch and devel-opment precision irrigation control system in agriculturalrdquoin Proceedings of the International Conference on Computerand Communication Technologies in Agriculture Engineering(CCTAE rsquo10) pp 117ndash120 June 2010
[17] E Garcia Modificaciones al Sistema de Clasificacion Climaticode Koppen Serie de Libros no 6 UNAM Instituto deGeografıaMexico City Mexico 5th edition 2004
International Journal of Distributed Sensor Networks 13
[18] K M Passino S Yurkovich and M Reinfrank Fuzzy Controlvol 42 Addison Wesley Longman Menlo Park Calif USA1998
[19] A Perez-Gutierrez A Pineda-Doporto L Latournerie-Moreno and C Godoy-Avila ldquoNiveles de evapotranspiracionpotencial en la produccion de chile habanerordquo Terra Latino-americana vol 26 no 1 pp 53ndash59 2008
[20] W C Q Ortiz A Perez-Gutierrez L L Moreno C May-LaraE R Sanchez and A J M Chacon ldquoUso de agua potencialhıdrico y rendimiento de chile habanero (Capsicum chinenseJacq)rdquo Revista Fitotecnia Mexicana vol 35 no 2 pp 155ndash1602012
The authors gratefully acknowledge the support of the Tech-nological Institute of Conkal ofMexico and Prometeo Projectof the Secretariat for Higher Education Science Technologyand Innovation of the Republic of Ecuador and CYTEDnetwork 514RT0486
References
[1] FAOSTAT FAO Statistical Databases amp Data-Sets Food andAgriculture Organization of the United Nations 2012 (Consul-tado Dic 2013) httpfaostatfaoorgsite567DesktopDefaultaspxPageID=567ancor
[2] SIAP Agri-Food and Fishing Information and Statistics ServiceSAGARPA Anuario estadıstico de la Produccion AgrıcolaMexico City Mexico 2011
[3] USDA and FAS ldquoGreenhouse and shade house production tocontinue increasingrdquo GAIN Report MX0024 vol 22 USDAFAS Mexico DF Mexico 2010
[4] H Turral M Svendsen and J M Faures ldquoInvesting in irriga-tion reviewing the past and looking to the futurerdquo AgriculturalWater Management vol 97 no 4 pp 551ndash560 2010
[5] S Tang Q Zhu X Zhou S Liu and M Wu ldquoA conceptionof digital agriculturerdquo in Proceedings of the IEEE InternationalGeoscience and Remote Sensing Symposium (IGARSS rsquo02) vol5 pp 3026ndash3028 June 2002
[6] L Bacci P Battista and B Rapi ldquoAn integrated method forirrigation scheduling of potted plantsrdquo Scientia Horticulturaevol 116 no 1 pp 89ndash97 2008
[7] R Lopez Lopez R Arteaga Ramırez M A Vazquez Pena ILopez Cruz and I Sanchez Cohen ldquoIndice de estres hıdricocomo un indicador del momento de riego en cultivos agrıcolasrdquoAgricultura Tecnica en Mexico vol 35 no 1 pp 97ndash111 2009
[8] N Livellara F Saavedra and E Salgado ldquoPlant based indicatorsfor irrigation scheduling in young cherry treesrdquo AgriculturalWater Management vol 98 no 4 pp 684ndash690 2011
[9] R Qiu S Kang F Li et al ldquoEnergy partitioning and evapotran-spiration of hot pepper grown in greenhouse with furrow anddrip irrigation methodsrdquo Scientia Horticulturae vol 129 no 4pp 790ndash797 2011
[10] J Casadesus M Mata J Marsal and J Girona ldquoA generalalgorithm for automated scheduling of drip irrigation in treecropsrdquo Computers and Electronics in Agriculture vol 83 pp 11ndash20 2012
[11] C O Akinbile and M S Yusoff ldquoGrowth yield and water usepattern of chilli pepper under different irrigation schedulingand managementrdquo Asian Journal of Agricultural Research vol5 no 2 pp 154ndash163 2011
[12] Y Huang Y Lan S J Thomson A Fang W C Hoffmann andR E Lacey ldquoDevelopment of soft computing and applicationsin agricultural and biological engineeringrdquo Computers andElectronics in Agriculture vol 71 no 2 pp 107ndash127 2010
[13] M Omid M Lashgari H Mobli R Alimardani S Mohtasebiand R Hesamifard ldquoDesign of fuzzy logic control systemincorporating human expert knowledge for combine harvesterrdquoExpert Systems with Applications vol 37 no 10 pp 7080ndash70852010
[14] A Merot and J-E Bergez ldquoIRRIGATE a dynamic integratedmodel combining a knowledge-based model and mechanisticbiophysical models for border irrigation managementrdquo Envi-ronmental Modelling and Software vol 25 no 4 pp 421ndash4322010
[15] S L Davis and M D Dukes ldquoIrrigation scheduling perfor-mance by evapotranspiration-based controllersrdquo AgriculturalWater Management vol 98 no 1 pp 19ndash28 2010
[16] Z Yao G Lou Z XiuLi and Q Zhao ldquoResearch and devel-opment precision irrigation control system in agriculturalrdquoin Proceedings of the International Conference on Computerand Communication Technologies in Agriculture Engineering(CCTAE rsquo10) pp 117ndash120 June 2010
[17] E Garcia Modificaciones al Sistema de Clasificacion Climaticode Koppen Serie de Libros no 6 UNAM Instituto deGeografıaMexico City Mexico 5th edition 2004
International Journal of Distributed Sensor Networks 13
[18] K M Passino S Yurkovich and M Reinfrank Fuzzy Controlvol 42 Addison Wesley Longman Menlo Park Calif USA1998
[19] A Perez-Gutierrez A Pineda-Doporto L Latournerie-Moreno and C Godoy-Avila ldquoNiveles de evapotranspiracionpotencial en la produccion de chile habanerordquo Terra Latino-americana vol 26 no 1 pp 53ndash59 2008
[20] W C Q Ortiz A Perez-Gutierrez L L Moreno C May-LaraE R Sanchez and A J M Chacon ldquoUso de agua potencialhıdrico y rendimiento de chile habanero (Capsicum chinenseJacq)rdquo Revista Fitotecnia Mexicana vol 35 no 2 pp 155ndash1602012
International Journal of Distributed Sensor Networks 13
[18] K M Passino S Yurkovich and M Reinfrank Fuzzy Controlvol 42 Addison Wesley Longman Menlo Park Calif USA1998
[19] A Perez-Gutierrez A Pineda-Doporto L Latournerie-Moreno and C Godoy-Avila ldquoNiveles de evapotranspiracionpotencial en la produccion de chile habanerordquo Terra Latino-americana vol 26 no 1 pp 53ndash59 2008
[20] W C Q Ortiz A Perez-Gutierrez L L Moreno C May-LaraE R Sanchez and A J M Chacon ldquoUso de agua potencialhıdrico y rendimiento de chile habanero (Capsicum chinenseJacq)rdquo Revista Fitotecnia Mexicana vol 35 no 2 pp 155ndash1602012
