Computers and Electronics in Agriculture 98 (2013) 233–241

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Computers and Electronics in Agriculture

journal homepage: www.elsevier .com/locate /compag

A fuzzy logic based irrigation system enhanced with wireless datalogging applied to the state of Qatar

0168-1699/$ - see front matter � 2013 Elsevier B.V. All rights reserved.http://dx.doi.org/10.1016/j.compag.2013.08.018

⇑ Corresponding author. Tel.: +974 66239471; fax: +974 44034201.E-mail address: touatif@qu.edu.qa (F. Touati).

Farid Touati a,⇑, Mohammed Al-Hitmi a, Kamel Benhmed a,b, Rohan Tabish a

a Department of Electrical Engineering, Qatar University, Qatarb Ecole Nationale d’Ingenieurs de Gabes (ENIG), Gabes, Tunisia

a r t i c l e i n f o a b s t r a c t

Article history:Received 12 November 2012Received in revised form 26 June 2013Accepted 19 August 2013

Keywords:Fuzzy logicIntelligent drip irrigationEvapotranspirationWireless monitoringArid region

In arid regions, developing environment and crop-specific irrigation scheduling that reduces water lostvia evapotranspiration is a key to a sustainable and better managed irrigation. This paper presents a prac-tical solution based on intelligent and effective system for a field of hyper aridity in Doha–Qatar. The sys-tem consists of a feedback fuzzy logic controller that logs key field parameters through specific sensorsand a Zigbee–GPRS remote monitoring and database platform. The system is easy to deploy in existingdrip irrigation systems without any physical modification. For a given crop, the fuzzy logic controlleracquires data from these sensors and then applies well-devised fuzzy rules to produce appropriate timeand duration for irrigation. All variables are fuzzified using trapezoidal and triangular membership func-tions. In this fuzzification, Max–Min inference engine and Mamdani-type rule base is adopted in order tomake the best decision for each situation. Typical data in summer and winter showed that the controllerensures maintaining the soil moisture above a pre-defined value with non-abrupt oscillations. The sys-tem compensates the amount of water that is lost through evapotranspiration as predicted by Pen-man–Monteith model and hence allows predicting future water consumption. A local station firstprocesses and saves real-time data received from the field controller via wireless Zigbee protocol tofinally transmit these data to a remote station via a GPRS link. This enhancement enables tracking systemperformance in real time and creating a database for analysis and improvement. It follows that thedeployment of fuzzy control combined with remote data logging would foster better management ofirrigation and water resources in hyper-arid lands such as Qatar.

� 2013 Elsevier B.V. All rights reserved.

1. Introduction

The importance of water is indisputable around the world andparticularly in arid and hyper arid lands. With the rapid industrialand social development, and the massive increase in Qatar’s popu-lation, conventional water resources have become seriously de-pleted. With an average of less than 250 m3 available per personper year, Qatar falls far below the internationally recognized ‘waterpoverty line’ of 1000 m3 per person per year (Al-Mohannadi et al.,2003; Hasim, 2009; Prakash and Chenb, 2010; Kanzari et al., 2012).Without proper management, water will become a severe con-straining factor in the socio-economic development of the country.Currently, agriculture accounts for about 60% of the total net de-mand on fresh water in Qatar (Alexandridis et al., 2008; FAO WaterReports, 2008; Feliu-Batlle et al., 2009; John, 2011). This demand isexpected to increase even further while ground water is continu-ously in depletion. It is thus obvious that any measure taken to

improve the efficiency of water usage in agriculture will have asignificant impact towards achieving water supply sustainability,and secure the availability of this very valuable resource to thecommunity.

In Qatar, wastage of water in irrigation is mainly caused by;first: the use of traditional techniques which are based on timerssuch as basins and furrows irrigation (Gillies and Smith, 2005),and second: the water loss through ground evaporation and croptranspiration (so-called evapotranspiration ET). In the first case, re-search has shown that people over-irrigate crops due to the misun-derstanding of seasonal water need or the impracticality ofupdating the irrigation schedule to reflect actual water needs ofthe landscape (Haley et al., 2007). In this scenario, people generallyadjust timers by observing the crop and irrigating when it looksstressed (qualitative). On the other hand, the loss by evapotranspi-ration is inevitable and accentuated by the hyper-arid environmentunder untapped ambient temperature and solar radiation of6 kW h/m2/day where daylight is about 4449 h/year (Touati et al.,2013). Here, there is a need for automated irrigation systems thatare able to deliver the exact quantity of water required by the cropfor proper irrigation while reducing ET losses.

234 F. Touati et al. / Computers and Electronics in Agriculture 98 (2013) 233–241

There are several studies discussing the pros and cons of open-loop and closed-loop control systems (McCreadya et al., 2009;Rahangadale and Choudhary, 2011; Obota and Inyama, 2013).According to (Wade and Waltz, 2004; Jaume et al., 2012), the mostdeployed method of irrigation control is the closed-loop whichsplits into two categories; feed-forward and feedback control. Inthe feedback control, the idea is to maintain soil moisture (i.e.plant’s water stress) within a specific range by measuring crop’sneeds from soil moisture levels using instruments such as tensiom-eters or dielectric probes (Javadi et al., 2009). However, in the feed-forward control (known as ET control), controllers use the crop’sreference evapotranspiration (ETo) to schedule irrigation compen-sating then for ET water loss through the water balance technique.Climatic conditions have direct influence on ETo (Davis and Dukes,2010), which can be calculated by using Penman Monteith modelas this has been officially adopted by the FAO (Allen et al., 1998;Roy et al., 2009; Yang et al., 2010). In hyper-arid lands like Qatarfor example, ETo can reach 10 mm day�1 in summer which corre-sponds to 10 L per square meter per day (Hasim, 2009). For bothfeedback and feed-forward approaches, in order to minimize ETlosses and hence system’s overrunning that would result in wast-age of water and energy, the controllers should schedule irrigationeither in the morning, around sunrise, or even at night (Speetjenset al., 2008; Car et al., 2012). This reveals the complexity of the irri-gation decision and stresses the need for a robust and effective irri-gation strategy especially in harsh environments whereevapotranspiration is acute. To address this complexity, muchattention has been paid to fuzzy theory in order to improve theability to make correct decisions.

Fuzzy logic interprets real uncertainties and becomes ideal fornonlinear, time-varying and heuristic knowledge to control a sys-tem. As contrasted to conventional feedback control systems, it isgaining importance due to its flexibility in handling imprecise sub-jective data and hence very effective for real-world decision-mak-ing problems (Zhang et al., 1996; Mirabbasi et al., 2008). Thefunctionality of fuzzy logic has been extensively tested in a widerange of applications. Jia et al. (2011) designed a field integrativeirrigation controller based on fuzzy logic and programmable logiccontroller. A fuzzy logic-based multi-criterion decision making ap-proach was applied for selecting the best-performing irrigationsubsystem in India (Raju and Kumar, 2005). A Fuzzy Logic Feed-back Controller (FLC) prototype based on a Mamdani controllerand simulated on MATLAB software was shown to be more effec-tive when compared to feedback controllers of simple on/off andon/off with hysterics (Javadi et al., 2009). A fuzzy logic based ben-efit-cost approach proved to be very suitable in decision makingbetween three alternative irrigation projects (Anagnostopoulosaand Petalasb, 2011). In Mexico, the development of a fuzzy irriga-tion control system using a field programmable gate array (FPGA)to control greenhouse fertigation was found cost-effective and easyto implement (Melendez et al., 2011). Rahangadale et al. (2011) re-ported that fuzzy logic control improves the performance of auto-matic irrigation systems by smoothing system ON/OFF and having

Fig. 1. Block diagram of sm

the potential for saving water when compared to conventionalcontrollers. Fuzzy logic was used to develop a model for crop waterstress index (Al-Faraj et al., 2011). In Qiu et al. (2007), a fuzzy irri-gation decision-making system established by using virtual instru-mentation platform of sensors, test instruments, data logger, andLabView was presented. Generally, published studies use ON/OFFcontrollers where the inherent complexity of irrigation processmade it difficult to achieve optimal results. Few studies used ETcontrollers, however, inputs for such systems which are used tocalculate the theoretical irrigation requirement (scheduling) arecomplex and subject to a lot of uncertainties rendering the sched-uling efficiency and irrigation adequacy a difficult task (Davis andDukes, 2010). Systems using feedback (or soil moisture) controllershave been used successfully. For example, an irrigation controllerwhich has been developed based on controlling soil moisture(Muñoz-Carpena et al., 2004) reduced irrigation water by 70% ondrip irrigated tomato in South Florida. Research shows that feed-back control based systems give promising results in terms ofwater savings as high as 70% compared to drip approach with nonegative impact on crop yields (Nogueira et al., 2003; Dukeset al., 2003; Dukes and Scholberg, 2005).

Most commercialized systems are ‘‘on/off with hysteresis’’ sys-tems, where the controller continually compares one input withtwo preset values to provide a decision either start or stop irrigat-ing. This approach is simple but cannot handle efficiently complexsystems with multiple inputs like irrigation. As discussed above,fuzzy logic is very flexible, robust by nature, has a clear reasoninglogic mimicking human brain (Domingo et al., 2011) and hencewell fit to this application.

In this study, we developed a site-specific standalone smart irri-gation system (SIS) which uses FLC-based feedback ON/OFF control.The SIS is designed to efficiently schedule irrigation for cucumberunder drip irrigation system in open fields in order to keep the soilmoisture above 17% and avoid times where evapotranspiration ishigh. In Qatar, the soil moisture that is adequate for cucumber isaround 66%, however, it should not go below 17% to avoid waterstress (shortage) and bitter taste in the yield (Hasim, 2009). TheFLC is supposed to provide the time and duration of irrigation asneeded by the crop in a 100-m2 field in Doha (coordinates:25.375�N, 51.490�E). Various sensors were deployed to log criticalfactors, such as soil moisture, ambient temperature, solar radiationand amount of water consumed. The readings from the various sen-sors were then fed to the FLC to apply well-devised fuzzy rules tocontrol irrigation. In order to monitor and save measured data in realtime for further analysis, the system was also enhanced with Zig-Bee–GPRS module for wide-range wireless monitoring and data log-ging platform. To the authors’ knowledge, this is the first studypublished on designing such irrigation system applied to Qatar.

2. System design

The system structure of SIS is shown in Fig. 1. It consists of twomain subsystems: (1) the sensing and control, and (2) long-range

art irrigation system.

5 Volts

Vout

R1=10 k

RLDR

Microcontroller Buffer

Fig. 3. LDR voltage divider circuit.

Fig. 4. Experimental and estimated output voltage of moisture sensor vs. soilmoisture.

F. Touati et al. / Computers and Electronics in Agriculture 98 (2013) 233–241 235

communication protocol and monitoring. The control part is devel-oped to be near to the field and a local (relay) station communi-cates with the controller and remote station via ZigBee and GPRScommunication protocols, respectively. A more detailed descrip-tion of the methodology in developing these subsystems is givenin the following subsections.

2.1. Sensing and control

The controller consists of specific field sensors, an FLC-embed-ded data logging and wireless communication board, valve andflow sensor, and power supply system.

2.1.1. Field sensors and calibrationThree sensors were used to log the environment, namely, solar

radiation (LDR), soil moisture (VG400RevD), and ambient temper-ature (LM35). These sensors are deemed most critical in the irriga-tion process and their readings are used by the FLC to devise thedifferent fuzzy rules (Boman et al., 2006). Although there are otherparameters like relative humidity and wind speed, in arid landsthese parameters have little effect (Liao et al., 2008).

Since the FLC relies on readings from sensors, these have to bechecked for integrity. As sensors drift with use and time, they haveto be calibrated before use whenever deemed necessary. The cali-bration results of the different sensors are discussed below.

2.1.1.1. LDR calibration. The resistance of the LDR decreases as thelight impinging on it increases and thus can be used to create asimple and low-cost photometric sensor. The LDR was calibratedusing a light meter (RS 180-7133). The calibration results are de-picted in Fig. 2 which spans a wide light intensity window fromdark to full sun light. The relationship between ambient light andLDR resistance is non-linear, and shows a straight line if drawnin a logarithm scale. A good fitting curve is given by followingequation:

log10ðRLDRÞ ¼ �0:5 � log10ðLightÞ þ 4:23 ð1Þ

When placed in a voltage divider circuit of Fig. 3, the LDR outputvoltage (Vout) which is given by Eq. (2) reflects the field solar radi-ation as follows.

Vout ¼ 5 � RLDR=ðRLDR þ R1Þ ð2Þ

Then, combining (1) and (2) gives Eq. (3) below which expresseslight as a function of Vout. It follows that the controller (i.e. micro-controller) can calculate the light intensity in the field by readingVout and plugging it into equation.

light ¼ 10 ^ 8:46� 2 � log104 þ Vout

5� Vout

! !ð3Þ

Fig. 2. Experimental and estimated LDR resistance vs. light intensity.

2.1.1.2. VG400RevD calibration. The VG400RevD sensor delivers amaximum of 3 V for 100% moisture when powered with 5 V. Cali-bration measurements in Fig. 4 show a non-linear behavior be-tween output voltage (Vout) and soil moisture (vwc). A goodestimate of a fitting curve between Vout and moisture is given by:

Vout ¼ 2:98 � ð1� e�ðvwc=7ÞÞ ð4Þ

The sum squared error between the experimental data and Eq.(4) was estimated to be about 0.182, which we consider acceptablein this application.

In addition, Fig. 4 shows that the dynamic range of the soilmoisture sensor used is around 35%. This means that an acquisitionsystem cannot discern between moistures above 35% since the soilmoisture sensor will provide same output voltages for moisturesabove 35% as shown in Fig. 4. In this application to schedule irriga-tion the FLC uses threshold moisture of 17% (see Fig. 5) which iswithin the sensor range.

2.1.1.3. LM35 calibration. The LM35 does not require any externalcalibration or trimming to provide typical accuracies of ±3/4 �Cover a full �55 to +150 �C temperature range. The LM35 has a lin-ear +10.0 mV/�C scale factor, to detect a maximum of 100 �C. Thecalibration curve is given by:

Vout ¼ 0:05 � temperature ð5Þ

To reduce conversion errors (conversion, linearity, drift), a con-ditioning block was used to match the sensor output to the analogfull-scale range of the microcontroller ADC. Eqs. (3)-(5) were usedat the local station to convert received data back to light, soil mois-ture and ambient temperature, respectively.

2.1.2. FLC-based data logging and wireless communication boardThe board is constructed around the ATmega32 microcontroller

which makes the system compact, deployable, adaptive, and scal-able. The main tasks of this board are data acquiring, processing,

Begin

Read: Temperature, Radiation and Moisture

Moisture > 17%

Calculate Duration from Fuzzy Logic Read Water Flow

Start irrigation

Read Water Flow

Wait for Duration

Stop irrigation

Send data via Xbee

Wait 30 minutes

Yes

No

Fig. 5. Flow chart software.

Table 1RULES for duration of irrigation (in min) at wet soil moisture.

Air temperature (�C) Solar radiation (Lux)

Light Medium Dark

Cold Zero Zero ZeroMedium Zero Zero ZeroHot Zero Zero Zero

Table 2RULES for duration of irrigation (in min) at medium soil moisture.

Air temperature (�C) Solar radiation (Lux)

Light Medium Dark

Cold Short Short ShortMedium Very short Short ShortHot Zero Very short Long

Table 3RULES for duration of irrigation (in min) at dry soil moisture.

Air temperature (�C) Solar radiation (Lux)

Light Medium Dark

Cold Very long Very long Very longMedium Short Long LongHot Zero Very short Very long

236 F. Touati et al. / Computers and Electronics in Agriculture 98 (2013) 233–241

logging, and transmitting. An application-specific FLC was devisedand loaded to the microcontroller. MAT Lab simulations and sys-tem reduction were performed on the FLC beforehand. Fuzzy setsand logic can handle real-life uncertainties hence ideal for as suchnonlinear, time-varying and hysteretic control system (Bellazziet al., 1998; Hahn, 2011). Detailed description of the FLC is givenin the subsection below. Also, the board is enhanced with Zigbeemodem for short-range wireless connectivity. In this application,the board is configured to transmit all sensors’ reading to the localstation every 30 min. The local station saves and in turn transmitsprocessed data to the remote station. This feature enables remotemonitoring and creating database of irrigation parameters, envi-ronmental conditions, and system performance for analysis andbetter management.

2.1.3. Water valve and flow sensorFor water supply, conventional on/off electro-valve was se-

lected because it consumes less current than proportional solenoidvalves. The on/off electro-valve was connected to a drip irrigationpipe line and controlled by the FLC. The current drawn by the elec-tro-valve is about 200 mA under 12-V supply. Therefore, a buffercircuit that ensures driving the electro-valve from one of themicrocontroller output port was used.

In order to record the amount of water being dispensed, a flowsensor (model FS-1F-SZ) which measures the water flow in L/min

was added to the system. This flow sensor output is binary andhence a frequency-to-voltage (F/V) converter was added. The out-put of F/V converter was fed to the microcontroller analog inputport. By recording the duration of irrigation, the local and remotestations calculate the amount of water being consumed. Thisamount is displayed and saved along with ambient temperature,soil moisture, and solar radiation readings. The flow sensor alsoalerts in case of shortage of water.

2.1.4. Solar power supplyThe control hardware was developed over a small printed cir-

cuit board and consumes a low current (max of 0.5 A) under a12-V supply. The whole system was run from a single solar100 W-PV (from PTL Solar Co.) charging a battery via a charge opti-mizer (PTL/CC-T/30). The solar power supply was designed so thatit can power the control subsystem continuously. The panel wastilted at an angle to match the latitude of the location. In orderto avoid panel heating and damage due to high irradiation levels(6 kW h/m2/day, untapped world’s greatest solar power potential),a slight shift from that angle is recommended. Since Qatar is lo-cated in the tropical cancer at latitude of 25� from the equator,the system was installed with a tilt angle of 23�. This set up al-lowed replenishing the battery enough to account for energy needof the system during the day. A critical unit in the SIS is the FLCwhich is described thoroughly in the subsection below.

2.2. Fuzzy logic control

Fig. 5 shows the flow chart of system operation. At the begin-ning, the FLC acquires readings from ambient temperature, solarradiation and soil moisture sensors to compute the fuzzy rules.The soil moisture sensor indicates if irrigation is needed. In thiscase study, if moisture falls below 17% then irrigation is triggered.However, the duration of irrigation is made by the FLC after carry-ing out a fuzzy reasoning and decision using the information fromthese sensors (see paragraph below). In any situation, the outcomewill be the time and duration of irrigation according to the

Input Data

Defuzzifier

Inference

Output Data

Fuzzifier

Rule Base

Fig. 6. Fuzzy logic controller structure.

irrigation duration (minutes)

Soil moisture (%)

Solar Radiation (Lux)

Temperature

Fig. 7. Inputs and output membership functions.

Fig. 8. Photos of the system: field controller and sensors with solar PV system.

F. Touati et al. / Computers and Electronics in Agriculture 98 (2013) 233–241 237

rule-base (see Tables 1–3). It is worth mentioning that the processof aspiration of water through the soil was estimated to be about30 min, thus the system was programmed to wait the same periodbefore starting next cycle. This accounts for the 30-min frequencyof data transmission. The program allocates 6.5 kilobytes in flashmemory, equivalent to 20.3% of the total memory (32 kilobytes)only.

Fig. 6 explains the structure of the FLC implemented here. Allvariables are fuzzified using trapezoidal and triangular membershipfunctions. The membership functions are distributed according tothe possible values of each variable after fuzzification. The fuzzifi-cation process, Max–Min inference engine and Mamdani-type rulebase produce the required decision for each situation. After appli-cation of a centroid defuzzification, the controller produces the de-sired output.

Three inputs (i/p) and one output (o/p) are used in this work:

Soil moisture (i/p#1): gives the water stress in soil. It is the mostimportant information to know whether or not irrigation isneeded and estimate the amount of water that should be dis-pensed to the crop.Outside temperature (i/p#2): measures ambient temperature.This input enables the FLC reducing water evaporation byavoiding irrigation at high ambient temperatures.Radiation (i/p#3): measures solar radiation in the field. Likewisethe ambient temperature input, since solar radiation acceler-ates evapotranspiration, it helps the FLC reducing water lossthrough evapotranspiration.Duration (o/p): the FLC provides the time and duration of irriga-tion through the output port.

Fig. 9.1. Real-time remote temperature and radiation data (May 2012).

Fig. 9.2. Real-time remote duration of irrigation and temperature data (May 2012).

Fig. 9.3. Real-time remote soil moisture and duration of irrigation data (May 2012).

238 F. Touati et al. / Computers and Electronics in Agriculture 98 (2013) 233–241

The devised inputs and output membership functions of FLC areshown in Fig. 7.

For the purpose of avoiding memory leaks and improving theprogram execution speed, three significant membership functionswere used for each input variable. The output variable needs tobe extended with five membership functions to cover the variationof water need.

Tables 1–3 show the designed fuzzy logic based rules that wereimplemented in the system. As an example of these rules:

� ‘‘If the soil is wet, then regardless of the temperature and radi-ation values, the irrigation duration is set to Zero’’.

� ‘‘If the soil moisture is medium, temperature also medium andradiation is light, then the irrigation duration is set to VeryShort’’.� ‘‘If the soil moisture is dry, temperature is hot while the radia-

tion is light, then the irrigation duration is set to Zero’’.

2.3. Long-range communication protocol and monitoring

End-to-end transmission of SIS data is assured by a Zigbee–GPRS platform. The communication between the field controllerand local station is done over a short-range (100 m line-of-sight)IEEE802.15.4 compliant Zigbee protocol (Xbee Series 1 chip) by vir-

Fig. 9.4. Real-time remote data of water consumption (May 2012).

Fig. 10.1. Real-time remote temperature and radiation data (January 2013).

Fig. 10.2. Real-time remote soil moisture and duration of irrigation data (January 2013).

F. Touati et al. / Computers and Electronics in Agriculture 98 (2013) 233–241 239

tue of its low-power feature. This would support long power sys-tem autonomy since the control subsystem is solar-powered. Inaddition, IEEE 802.15.4 ‘‘Zigbee’’ compliant chips present uniquefeatures for conventional and precision agriculture (Garcia-San-chez et al., 2011). The local station receives, processes, and savesdata from the field and then routs this data to a remote station(i.e. a PC) via wide coverage GPRS/GSM modules on both sides.The GPRS/GSM module is interfaced to the remote station via amicrocontroller over UART. All data are saved and displayed inreal-time at the remote station using the graphical software Lab-

View. The data at the local and remote stations when comparedfor conformity, revealed no differences.

In fact, in an actual scenario, there is no local monitoring forlarge systems because it is not practical to deploy monitors (e.g.PCs) within every 100 m range. The purpose of the local stationis to provide connectivity between the short-range and long-range networks. This local station is generally AC-powered fromthe sector and meant to remain turn-on all the time. Also, thelocal station permits system setting and testing during deploy-ment phases.

Fig. 10.3. Real-time remote data of water consumption (January 2013).

240 F. Touati et al. / Computers and Electronics in Agriculture 98 (2013) 233–241

Fig. 8 depicts shots of the proposed developed system includingthe field controller, sensors and solar PV supply. The system can beeasily implemented into existing drip-type irrigation systemswithout any logistic or physical modifications.

3. Results and discussion

To examine the system’s behavior, typical real-time data col-lected remotely at two different seasons; summer (May 2012)and winter (January 2013), are illustrated in Figs. 9.1–9.4 and10.1–10.3, respectively. In May and June, the average temperatureis around 40 �C and 20 �C and the duration of luminance is about14 and 10 h of daylight, respectively. The curves show actual mea-surements of a typical field plant in Doha–Qatar. The reliability ofthese data was verified by comparing them to on-site and local sta-tion data.

As expected, Figs. 9.1–9.3 and 10.1–10.3 showed that the FLCavoids operating the system during high temperatures, which cor-responds to high solar radiation (Figs. 9.1 and 10.1). This contrib-utes in reducing evapotranspiration losses throughout the day.Also, the FLC selects the appropriate time and duration by referringto the base of fuzzy rules (Figs. 9.2 and 10.2). The actual soil mois-ture tracks the desired value (17%) with non-abrupt oscillations ascompared to traditional feedback systems, avoiding frequent sys-tem runoff of such ON/OFF systems (Figs. 9.3 and 10.2).

Fig. 9.4 shows that in 6 days the total amount of water con-sumed was 6252 L, that is, an average of 1042 L per day over anarea of 100 m2. It follows that our control system irrigated the fieldwith an average of 10.42 L/day per square meter, that is,10.42 mm day�1. Interestingly, this is similar to the evapotranspi-ration value for arid lands in summer; 10.00 mm day�1 as reportedin (Hasim, 2009). The same calculation was made using the data inFig. 10.3 (January) which resulted in an average irrigation of6.70 mm day�1 whereas the evapotranspiration calculated byFAO Penman–Monteith model in January is around 3.72 mm day�1

(Hasim, 2009). This indicates that in winter the system compen-sated ET losses with an over irrigation. Over-irrigation has alsobeen reported for systems using ET controllers as reported by Davisand Dukes (2010). Overall, the above results show that the fuzzycontrol-based irrigation system developed here compensates effi-ciently ET losses in hyper-arid regions like Qatar for a well-wateredand actively-growing crop. The system enables predicting futurewater needs and hence fosters better irrigation management. Thiswould require the development of appropriate artificial intelli-gence algorithms (e.g. ANN) that are trained over wide time win-dows. Our team is currently embarking on developing suchalgorithms along with scaling up the system to larger fields.

A preliminary study showed that compared to the current irri-gation techniques in Qatar (surface flooding and furrow), the cur-rent SIS system saves up to 80% of water consumption, equal toaround 1521320.00 L per year over an area of 100 m2. For the user,this translates locally to around $1825.6 of net profit per year forhighly subsidized price of about $1.20/m3, about 35% of the realcost (Darwish and Mohtar, 2013). This looks very beneficial vis avis a total system cost of around $400.00. The developed systemis low-cost, efficient, and easy to implement. This is in additionto the benefit of preserving the groundwater aquifer which is cur-rently considered a ‘‘first-order resource’’, that means it is a re-source which is becoming scarcer in the country.

4. Conclusions

In this paper, feedback fuzzy control and wireless monitoringwere presented as a strategy to develop an irrigation approach thatfosters water conservation and better irrigation management inhyper-arid lands like Qatar. The developed fuzzy controller, basedon Mamdani fuzzification using trapezoidal and triangular member-ship functions, efficiently set the time and duration of irrigationfor a given crop. The use of fuzzy control helped maintaining thesoil moisture above a pre-set value with smooth oscillations pre-venting hence frequent system’s run-off and preserving waterand energy. The system compensated the amount of water thatwas lost by evapotranspiration as predicted by Penman–Monteithmodel in summer and winter with a noticeable over-irrigation inwinter. Therefore, the system enables predicting future irrigationneeds. In order to monitor system performance in real time andcreate a database, a wide-range ZigBee–GPRS based wireless sys-tem was also developed. Key environmental and climatic factorsalong with the time and duration of irrigation as well as theamount of water being sourced were recorded. Currently, this ishelping a great deal in data analysis and system improvement.The system is easy to implement. A preliminary cost-benefit anal-ysis showed that the system is economically justifiable.

Acknowledgment

This research has been supported by Qatar University projectGrant (QUUG-ENG-DEC-10/11-13).

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