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DEVELOPMENT OF AN AGRO-METEOROLOGICAL MONITORING
APPLICATION USING GEO-SPATIAL TECHNIQUES FOR PRECISION
FARMING
(CASE OF STUDY OF MAPS, WIND SPEED, SUNRISE AND SUNSET)
Akerele F.O. , Adeyanju O. O. , Abiola, O. S, Akhire C.N.
[email protected], [email protected],
[email protected], [email protected]
1, 2 and 3 Department of Agricultural and Bio-environmental Engineering, Federal
Polytechnic, Ado Ekiti, Ekiti State, Nigeria
4 Joint Professional Training and Support International, Lagos State, Nigeria.
GSM:-1+2348035807570,
2+2348111399383,
3+2348035667356
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International Journal of Scientific Research and Innovative Technology Vol. 6 No. 2; February 2019
ABSTRACT
This project is based on the use of geospatial techniques for precision farming. Precision farming
is an approach to farm management using information technology which gives plant actual
information it needs for its optimum productivity and health. To enhance any farming production
and activities, precision farming is needed. Weather affects plants a lot in agriculture, in other to
know the actual weather information (map, wind speed, sunrise and sunset) needed on crop for its
healthy production, siting a farm house, and so on; precision farming/agriculture is a need for
adoption. Based on this, there was a need to develop an android application that will predict
current weather and get its information; basically (maps, wind speed and sunrise and sunset) based
on the user’s current location. The implemented package was tested on available mobile devices;
and analyzed how the mobile application developed can be used to enhance precision farming and
help the farmers boost their output.
To achieve this, the thesis focus on how geospatial data is collected, analyzed and used in the
decision making process (decision support system) to enhance agricultural productivity on yields.
To make this successful, geospatial data, an agro-meteorological monitoring application using
geo-spatial techniques for precision farming was developed. The growing of plant is important
where weather environment is favorable to crop for good harvest, because some crops need to be
protected against unfavorable weather condition from time of sowing to harvest time. The use of
this application developed enables the farmer or the user of the application to have the knowledge
and idea in order to make wise decision on which crop to plant on the farm from the data obtained
from the app developed for wind speed and sunrise and sunset in Ekiti State. Statistical tools used
to analyze the data were ANOVA and chart which shows the results are at variance and this is due
to the location of the Automatic weather station because some sited theirs around river, hill, snow,
farm land e.t.c.
Key-Words: - Agro-meteorological, Geo-spatial, Map, Precision farming, Sunrise and
Sunset, Wind speed
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1. INTRODUCTION
As the world's population grows, farmers will need to produce more and more food. Yet arable
acreage cannot keep pace, and the looming food security threat could easily devolve into regional
or even global instability. To adapt, large farms are increasingly exploiting precision farming to
increase yields,
In the past, precision agriculture was limited to larger operations which could support the IT
infrastructure and other technology resources required to fully implement and benefit from the
benefits of precision agriculture. Today, however, mobile apps, smart sensors, drones and cloud
computing makes precision agriculture possible for farming cooperatives and even small family
farms.
Every year copious technologies have been applied by many researchers, agronomies, scientist and
engineers to increase agricultural production with low cost, but it has adverse impact on
environment. Precision agriculture deals with the study of the application of technology to improve
agricultural practices as compare to conventional agricultural method and lower adverse impact on
environment. Remote sensing technology plays an important role in precision agriculture and its
application in the precision agriculture introduces new opportunities for improving agricultural
practices. With the help of global positioning system (GPS), it is possible to record field data
(slope, aspect, nutrients, and yield) as geographically latitude and longitude data (bonvioganmi and
lowenberg-deBoer, 2004; Roberts et al., 2004; Torbett et al., 2007; Watson et al., 2005).
It has capability to determine and record the correct position continuously, so therefore, it can
create a larger database for the user. For the further analysis, geographic information system (GIS)
is required, which can store and handling these data. This thesis highlights about remote sensing
technology, GIS, GPS and give you an idea about, how it can be valuable in precision agriculture.
Accurate and timely information is necessary to evolve strategies for sustainable management of
natural resources. Today’s “Space Age” supported by computer and communication technologies
offer great scope for efficient planning and management of agricultural resources on scientific
principles. The satellite data hitherto was considered as sensitive and used mostly for defence
purposes. However, the space scientists are now willing to share the satellite data, although on a
high cost basis, for civilian use.
For the precision farming, remote sensing (RS) and geographic information system (GIS)
technologies have been great of use to planners in planning for efficient use natural resources.
Also, agricultural meteorological encompasses meteorological, hydrological, and biological factors
that have an effect on agricultural production. It is also concerned with the interaction between
agriculture and environment. Agro-meteorological method therefore uses information and data
from different key sciences such as soil physics and chemistry, hydrology, meteorology, crop and
animal and others. For examples, new method for spatial applications involves GIS and remote
sensing for spatial data presentation and generation.
There is a need of knowledge and skill on how to transform, through geographic information
system (GIS), data collected by different sensors and geo-spatial into maps to provide on crop
physiological status and soil condition status.
Therefore, a development of an application for an android software was done in which its work is
to give exact and accurate information of map, wind speed, sunrise and sunset.
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International Journal of Scientific Research and Innovative Technology Vol. 6 No. 2; February 2019
In this research android mobile application software that will be able to predict the actual and get
the correct weather information to make farming system easy was developed and tested.
2. Precision Agriculture (PA)
Precision agriculture (PA), satellite farming or site specific crop management (SSCM) is a farming
management concept based on observing, measuring and responding to inter and intra-field
variability in crops. Perhaps the easiest way to understand precision agriculture is to think of it as
everything that makes the practice of farming more accurate and controlled when it comes to the
growing of crops and raising livestock. Precision farming allows the precise tracking and tuning of
production. Precision farming makes farm planning both easier and more complex. This support the
IT infrastructure and other technology resources required to fully implement and benefit from the
benefits of precision agriculture. Today, however, mobile apps, smart sensors, drones and cloud
computing makes precision agriculture possible for farming cooperatives and even small family
farms.
2.1 Wind speed
Wind speed is the speed of the weather related air movement from one place to the next. Wind
speeds usually mean the movement of air in an outside environment, but the speed of movement of
air inside is also important in many cases, including weather forecasting, aircraft and maritime
operations, construction and civil engineering. High wind speeds can cause unpleasant side effects,
and strong winds often have special names, including gales, hurricanes, and typhoons. The highest
wind speed ever measured on earth, 231 miles per hour, was recorded on Mount Washington (New
Hampshire). You can measure wind speed and wind direction with a variety of wind vanes and
anemometers (wikipedia.org).
2.1.1 Importance of wind speed to agriculture
Wind speed has great benefit on agriculture, some of which are stated below:
• Wind increases the turbulence in atmosphere, thus increasing the supply of carbon- dioxide
to the plants resulting in greater photosynthesis rates.
• Wind alters the balance of hormones.
• Wind increases the ethylene production in barley and rice.
• Wind decreases gibberillic acid content of roots and shoots in rice.
• Nitrogen concentration in both barley and rice increase with increase in wind speed.
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2.2 Sunrise and Sunset
Sunset colours are typically more brilliant than sunrise colours, because the evening air contains
more particles than morning air. Sunset occurs when the upper edge of the Sun – called the upper
limb sinks just under the horizon; sunrise occurs when the upper limb rises just above the horizon.
Sunrise or sun up is the moment when the upper limb of the sun appears on the horizon in the
morning. The term can also refer to the entire process of the solar disk crossing the horizon and its
accompanying atmospheric effects.
Sunset or sundown is the daily disappearance of the Sun below the horizon due to Earth's. The time
of sunset is defined in astronomy as the moment when the upper limb of the Sun disappears below
the horizon.
2.2.1 Importance of sunrise and sunset to agriculture
Sunrise has great benefit on agriculture as stated below:
• Solar energy provides light required for seed germination, leaf expansion, growth of stem
and shoot, and flowering, fruiting and thermal conditions necessary for the physiological
functions of the plant.
• Solar energy plays an important role as regulator and controller of growth and development.
• Solar radiation also influences assimilation of nutrient and dry matter distribution.
2.3 Basic Programming
A program is a set of instructions written in a language (such as BASIC) understandable by the
computer to perform a particular function on the computer.
Programming languages are artificial notational languages created or developed to be used
in preparing coded instructions on the computer for later execution by the computer. When
programming, we relate directly to the computer and also coding the software application to be
developed. There are two types of applications: (a) System applications: these are applications
embedded on a system (phones) e.g. calculator, message, radio, etc.
(b) Software applications: these are basically installed for personal use, e.g. AutoCAD, PowerPoint,
etc.
2.4 Applied Geospatial Techniques
Geographical Information Systems (GIS) links location based information (spatial) to database
information (tabular) enabling the user to visualize patterns, relationships and trends. This means of
analysis grants a new perspective to information, which is practically absent from exclusively
tabular data. Using GIS, we can manage, analyze, query and interact with geographically
referenced information using Spatial Analysis techniques. Spatial Analysis itself is the study of the
distribution and clustering of events and/or objects in space, in conjunction with their attribute
characteristics.
Geospatial Techniques. Geographers employ a number of different techniques for collecting,
studying, and analyzing spatial data. These techniques include both quantitative and qualitative
approaches, while also stressing important computer-centered technologies. Geospatial techniques
together with remote sensing, geographic information science, Global Positioning System (GPS),
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cartography, geovisualization, and spatial statistics are being used to capture, store, manipulate and
analyze to understand complex situations to solve mysteries of the universe.
Geographical Information Systems (GIS) links location based information (spatial) to database
information (tabular) enabling the user to visualize patterns, relationships and trends. This means of
analysis grants a new perspective to information, which is practically absent from exclusively
tabular data.
3. METHODOLOGY
This work started on the premise of the previous work done in 2016 by Akerele et al., (2016).
These authors have successfully developed Gumbel Mathematical model for predicting
meteorological data in agriculture.
3.1 Development of an Android Software Application
This is the process by which new applications are created for devices running the Android
operating system. Officially, apps can be written using Java, C++ or Kotlin using the Android
software development kit (SDK).
But in this case, java programming was adopted using the Android software development kit
(SDK) as shown in Plate 1. The Android software development kit (SDK) includes a
comprehensive set of development tools. These include a debugger, libraries, documentation,
sample code, and tutorials. Currently supported development platforms include computers running
Linux (any modern desktop Linux distribution), Mac OS X 10.5.8 or later, and Windows 7 or later.
Plate 1: Android Studio Setup
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The show details button will let you view detailed information about the installation progress. The
dialog box shown in Plate 2 will inform you when installation has finished. When you click next,
you should see the following:
Plate 2: Android Studio Setup
3.2 Running Android Studio
Android Studio presents a splash screen when it starts running, on the first run, it will be asked to
respond to several configuration-oriented dialog boxes. The first dialog box focuses on importing
settings from any previously installed version of Android Studio.
If no previous installed version available, just keep the defaults setting.
Finally, click finish to complete the wizard. You should see the Welcome to Android Studio dialog
box as shown in plate 3;
Plate 3: Android Studio Set Up
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International Journal of Scientific Research and Innovative Technology Vol. 6 No. 2; February 2019
3.3 The Development Step Using Android Studio
In order to complete the development action for the app, the followings were considered:
(i) A weather API key was gotten from the openweathermap which is an online service that
provides weather data, including current weather data.
(ii) After this API key was gotten, it was parse into the java programming used in order for the
android device debugging, this was done; enabling USB debugging in the developer options was
done by enabling the developer options.
Note: A developer mode in Android phones that allows newly programmed apps to be copied via
USB to the device for testing and a gps enabled phone with internet permission.
The following activities were developed in the android studio which each has enabling functions;
• Get location: this shows the latitude and longitude of exactly where the user is with the help
of the enabled GPS on the device alongside with the java programme language.
• Weather information: this is also shows the weather information of the location of where the
user is, which works together with latitude and longitude, and also with the help of the
registered agriculture API gotten from the openweathermap. The java language
programmed weather information is wind speed, sunrise and sunset.
• Map: this shows the exact map of the location where the user is.
On android studio, the Google map activity was chosen, this activity enables to be able to view real
time map on our device alongside the user location. The map activity code was written as well.
The above was done in the android studio in which each of the above button code was written and
in java programming language.
3.4 Layout
A new android project was chosen in the android studio where Application Name was changed to
"ABE one”, Company Domain: "example.com", preferred Android version was chose like API: 16
Android 4.1 (Jelly Bean), while maximum and minimum SDK was chosen. After all this has been
done, it should be in this following files; app > java > com.example.ABE one> MainActivity.
3.5 Editing the Manifest
The app will need a permission in which the next activity will be able to function, because we are
getting our data online, internet permission will be needed, to edit the manifest, go to app >
manifests > AndroidManifest.xml
The only permission this app needs is android.permission.INTERNET, and the java code was
written to enable internet permission.
4. RESULTS AND DISCUSSIONS
4.1 Results
The application was develop with a java programming language in android studio with the help of
software development kit (SDK) present in the android studio and was tested in an android mobile
device, because the application was developed to work in an android mobile device, in which the
data gotten from the application developed gives the average weather information (map, wind
speed, sunrise and sunset) for Ado – Ekiti.
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The developed application was connected to an automatic weather station (AWS) which is the
openweathermap station, with the help of the registered agriculture application programming
interface (API) key gotten from the openweathermap station; the application developed has the
access to the weather information on the station. With the android mobile device global positioning
system (GPS) turned on, the average weather information for the location which is Ado – Ekiti will
be gotten on the application developed.
The application was developed to enhance precision farming, for farm management approach
through information technology which provides actual information which plants and crop needs for
their optimum productivity and for healthy yield. This application developed will helps the farmer
to make a wise decision on what to do on the farm, and gives opportunity for smart farming.
Plate 4, 5 and 6 below shows the screenshots of the application developed when it is been operated.
The screenshots shows the activities carried out on the application developed which are; get
location, weather information and maps.
Plate 4: Location Coordinates Plate 5: Average Weather Information
Plate 6: Screenshot of the Map Location
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Plate 4, shows the location activity when the get location button is been clicked, which shows the
latitude and longitude coordinates of Ado – Ekiti, Plate 5, shows the average weather information
for Ado – Ekiti for wind speed, sunrise and sunset.
Plate 6, shows the map activity when the map button is clicked, and also shows the exact location
with the other locations on the map which is also Ado- Ekiti and inside the Federal Polytechnic
campus, Ado- Ekiti. Plate 4, 5, and 6 above was carried out with the below schematic diagram
illustration;
Plate 7: Schematic Illustration
The above schematic illustration in Plate 7 shows how the above button activity are being process;
there is an automatic weather station which the application developed is connected to, with the web
service, there is a registered API key which is gotten from the openweathermap which has being
input into the java programming language used to develop the application and enable the
application developed having a direct access to the weather information from the connected
automatic weather station, with a GPS and a satellite internet enabled mobile phone, the android
mobile application phone gets weather information and the last activity will be displayed which is
the map of the area where it is used as shown in Plate 7.
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4.1.1 Development Process
Figure 1 shows the development process of the app.
AWS
OPENWEATHERRA WEATHER
STATION
GPS
API KEY
PC
MAP API
KEY
XML JAVA
RUN AND
DEPLOY
ACTIVITIES
ANDROID
DEVICE
Figure 1: Development Process
International Journal of Scientific Rese
4.1.2 Results of the obtained dat
a connected automatic we
Below shows the results of the D
obtained from another automatic we
were collected for 4 months (July –
Appendix 1 shows the data obtain
openweathermap for July for wind
weather stations (yandexweather, fr
4.1.3 Average July wind speed d
weather station
Appendix 2 shows average data f
comparison with other two conne
Figure 2 below shows the graph for
and other two automatic weather sta
JULY AVERA
SPE
Figure 2: Average Wind Speed Fo
The blue colour on Figure 2 above r
and it was represented as “ABE”, w
green colour for the third weather
recorded an average wind speed of
ABE recorded an average wind spe
week 3, ABE recorded an average
m/s, while in week 4, ABE record
Freemeteo 4.95 m/s.
(m/
s
6
5
s p e e d
3
4
Wi
nd
2
1
0
WEEK 1
ABE
3.73
YANDEX
3.55
FREEMETEO 3.52
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earch and Innovative Technology Vol. 6 No. 2
ta from the application developed and other d
eather station
Data obtained from the application developed
eather station which is yandexweather, and free
October).
ined from the application developed which w
d speed and its comparison with other two conn
reemeteo).
data obtained from the application developed
for July wind speed from the application dev
ected automatic weather stations (yandexweat
or July average data obtained between the applic
ations.
RAGE WIND
EED
or The Month Of July For Ado Ekiti
represent the average data obtain from the applic
while the red colour for the second weather sta
station. In week 1, the following readings were
3.73 m/s, Yandex 3.55 m/s and Freemeteo 3.52
eed of 3.82 m/s, Yandex 3.40 m/s and Freeme
wind speed of 3.52 m/s, Yandex 3.38 m/s and
ded an average wind speed of 3.36 m/s, Yande
WEEK 2 WEEK 3 WEEK 4
3.82 3.52 3.36
3.4 3.38 3.26
4.24 4.19 4.95
2; February 2019
data from
d and other data
emeteo. This data
was connected to
nected automatic
and other
veloped and the
ther, freemeteo).
cation developed
cation developed
ation and the last
e obtained: ABE
2 m/s. In week 2
eteo 4.24 m/s. In
d Freemeteo 4.19
ex 3.26 m/s and
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In week 1, ABE has the highest wind speed, while in week 2, 3 and 4, freemeteo have the highest
wind speed. This variation in the results is due to the location where the AWS was positioned.
4.1.4 August wind speed data (App (Openweathermap), Yandexweather, Freemeteo).
Appendix 3 shows the data for August wind speed from the application developed and the
comparison with other two connected automatic weather stations (yandexweather, freemeteo).
4.1.5 Average table for august data obtained from the application developed and other
weather station
Appendix 4 shows average data for July wind speed from the application developed and the
comparison with other two connected automatic weather stations (yandexweather, freemeteo).
Figure 3 below shows the graoh for August average data obtained between the application
developed and other two automatic weather stations.
AUGUST AVERAGE WIND SPEED
6
2 YANDEX
0 FREEMETEO
WEEK 1 WEEK 2 WEEK 3 WEEK 4
Figure 3: Average Wind Speed For The Month Of August For Ado Ekiti
The blue colour on figure 3 represent the average data obtain from the application developed and it
is represents as (ABE), while the red colour for the second weather station and the last green colour
for the third weather station. For the month of August, week 1 has the highest wind speed, followed
by week 2, 3 and 4 Freemeteo recorded the highest wind speed.
4.1.6 September wind speed data app (openweathermap, yandexweather, freemeteo).
Appendix 5 shows the data for September wind speed from the application developed and the
comparison with other two connected automatic weather stations (yandexweather, freemeteo).
4.1.7 Average table for september data obtained from the application developed and
other weather station
Appendix 6 shows the average data for September wind speed from the application developed and
the comparison with other two connected automatic weather stations (yandexweather, freemeteo).
Figure 4 below shows the chart for September average data obtained between the application
developed and other two automatic weather stations. Freemeteo recorded the highest average wind
speed for all the weeks, followed by Yandex in week 2, 3 and 4.
4
ABE
Win
d s
pe
ed
(m
/s)
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SEPTEMBER AVERAGE WIND SPEED
6
5
4
3 ABE
2 YANDEX
1 FREEMETEO
0
WEEK 1 WEEK 2
WEEK 3
WEEK 4
Figure 4: Average Wind Speed For The Month Of September For Ado Ekiti
4.1.8 October wind speed data app (openweathermap), yandexweather, freemeteo and from
Nigeria meteorological station (nimet) ado ekiti.
Appendix 7 shows the data for October wind speed from the application developed and the
comparison with other two connected automatic weather stations (NIMET, yandexweather,
freemeteo).
4.1.9 Average table for october data obtained from the application developed and other
weather station
Appendix 8 shows the average data for October wind speed from the application developed and the
comparison with other two connected automatic weather stations (yandexweather, freemeteo).
Figure 5 below shows the graph of October average data obtained between the application
developed and other three automatic weather stations. In all the weeks, Freemeteo recorded the
highest wind speed, followed by Yandex, Abe and Nimet recorded the least wind speed.
Win
d s
pe
ed
(m
/s)
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OCTOBER AVERAGE WIND SPEED
5
4.5
4
3.5
3
2.5
2
1.5
1
0.5
0
1 2 3 4
WEEKS
Figure 5: Average Wind Speed For The Month Of October For Ado Ekiti
Win
d s
pe
ed
(m
/s)
ABE
NIMET
YANDEX
FREEMETEO
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4.1.10 Statistical analysis for data obtained
Below shows the statistical analysis for the data obtained using ANOVA and correlation for wind
speed.
Table 1: Anova Statistical Analysis For Data Obtained Between The App Developed And
Other Two Data Obtained From A Connected Automatic Weather Station For Wind Speed.
Anova: Single Factor
SUMMARY
Groups Count Sum Average Variance
ABE(OPENWEATHERM
AP) 123 394.28 3.205528
1.06599
9
YANDEXWEATHER 123 408.8 3.323577
0.04427
6
FREEMETEO 123 534.35 4.344309
3.34707
1
ANOVA
Source of Variation SS Df MS F P-value F crit
Between Groups 96.45864 2 48.22932
32.4605
7
1.05E-
13 3.020387
Within Groups 543.7961 366 1.485782
Total 640.2547 368
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Table 2: The Correlation between the Data Obtained from the App Developed and Other
Two Data Obtained from a Connected Automatic Weather Station for Wind
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4.1.11 July Data Obtained For Sunrise And Sunset From App. Developed
The data obtained for July from the app for sunrise and sunset was measured based on the sun
intensity and measured in watt meter per square (w/). The sunrise and sunset data obtained from the
app. developed is the average sunrise and sunset for ado Ekiti. Table 3 below shows the sunrise and
sunset data obtained from the application developed for the month of July.
Table 3: July Sunrise And Sunset Data Obtained From The App Developed
DATE SUNRISE
(w/).
SUNSET
(w/).
1/7/2018 1530422767 1530467973
2/7/2018 1530509175 1530554375
3/7/2018 1530519175 1530727193
4/7/2018 1530682004 1530554375
5/7/2018 1530768422 1530813601
6/7/2018 1530682004 1531554375
7/7/2018 1530994125 1530986413
8/7/2018 1531027664 1531072816
9/7/2018 1531114073 1531159216
10/7/2018 1531200489 1531245623
11/7/2018 1531286909 1531332032
12/7/2018 1531373319 1531418428
13/7/2018 1531459729 1531504828
14/7/2018 1531546139 1531591229
15/7/2018 1531632555 1531677628
16/7/2018 1531718969 1531764027
17/7/2018 1531805380 1531850425
18/7/2018 1531891787 1531936820
19/7/2018 1531978200 1532023219
20/7/2018 1531818669 1531850425
21/7/2018 1532151019 1532196011
22/7/2018 1532237432 1532282403
23/7/2018 1532323843 1532368796
24/7/2018 1532410245 1532455194
25/7/2018 1532496659 1532541582
26/7/2018 1532583079 1532627984
27/7/2018 1532669475 1532714362
28/7/2018 1532755885 1532800754
29/7/2018 1532842289 1532887153
30/7/2018 1532928697 1532973534
31/7/2018 1533015099 1533059920
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4.1.12 August Data Obtained For Sunrise And Sunset From The App. Developed
The data obtained for the month of august from the app for sunrise and sunset was measured based
on the sun intensity and it is measured in watt meter per square (w/). The sunrise and sunset data
obtained from the app. developed is the average sunrise and sunset for ado Ekiti. Table 4 below
shows the sunrise and sunset data obtained from the application developed for the month of august.
Table 4: August Sunrise And Sunset Data Obtained From The App Developed
DATE SUNRISE
(w/).
SUNSET
(w/).
1/8/2018 1533101503 1533146305
2/8/2018 1533187911 1533232690
3/8/2018 1533197503 1533130905
4/8/2018 1533360720 1533405460
5/8/2018 1533447124 153349184
6/8/2018 1533447125 1533491839
7/8/2018 1533619929 1533664615
8/8/2018 1533706330 1533751002
9/8/2018 1533792735 1533837385
10/8/2018 1533879135 1533923750
11/8/2018 1533965538 1534010132
12/8/2018 1534051938 1534096517
13/8/2018 1534138336 1534182898
14/8/2018 1534138338 1534182884
15/8/2018 1534311138 1534355659
16/8/2018 1534397536 1534442022
17/8/2018 1534483934 1534528398
18/8/2018 1534570332 1534614774
19/8/2018 1534383934 1534442026
20/8/2018 1534656728 1534701140
21/8/2018 1534829523 1534873897
22/8/2018 1534915918 1534960263
23/8/2018 1535002316 1535046644
24/8/2018 1535088709 1535133017
25/8/2018 1535175105 1535219383
26/8/2018 1535261503 1535305758
27/8/2018 1535347895 1535392124
28/8/2018 1535434292 1535478508
29/8/2018 1535520681 1535564867
30/8/2018 1535607079 1535651244
31/8/2018 1535693464 1535737593
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4.1.13 September data obtained for sunrise and sunset from app. developed
The data obtained for the month of September from the app for sunrise and sunset was measured
based on the sun intensity and it was measured in watt meter per square (w/).
The sunrise and sunset data obtained from the app. developed is the average sunrise and sunset for
ado Ekiti. Table 5 below shows the sunrise and sunset data obtained from the application developed
for the month of September.
Table 5: September Sunrise and Sunset Data Obtained From The App Developed
DATE SUNRISE
(w/).
SUNSET
(w/).
1/9/2018 1535779861 1535823979
2/9/2018 1535779870 1535823973
3/9/2018 1535952647 1535996711
4/9/2018 1536039040 1536083081
5/9/2018 1536125436 1536169450
6/9/2018 1536211823 1536255811
7/9/2018 1536298215 1536342178
8/9/2018 1536384606 1536428543
9/9/2018 1536470999 1536514908
10/9/2018 1536557390 1536601273
11/9/2018 1536643780 1536687645
12/9/2018 1536730179 1536774033
13/9/2018 1536816574 1536860401
14/9/2018 1536902955 1536946740
15/9/2018 1536989346 1537033113
16/9/2018 1537075739 1537119487
17/9/2018 1537093463 1537094863
18/9/2018 1537248520 1537292214
19/9/2018 1537334916 1537378592
20/9/2018 1537421299 1537464936
21/9/2018 1537421298 1537464931
22/9/2018 1537594085 1537637680
23/9/2018 15376804477 1537724053
24/9/2018 1537766864 1537810406
25/9/2018 1537853261 1537896789
26/9/2018 1537939652 1537983153
27/9/2018 1538026043 1538069516
28/9/2018 1538112433 1538155874
29/9/2018 1538198828 1538242252
30/9/2018 1538285217 153832860
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4.1.14 October data obtained for sunrise and sunset from App. developed
The data obtained for the month of October from the app for sunrise and sunset was measured
based on the sun intensity and measured in watt meter per square (w/).The sunrise and sunset data
obtained from the app. developed is the average sunrise and sunset of Ado Ekiti. Table 6 below
shows the sunrise and sunset data obtained from the application developed for the month of
October.
Table 6: October Sunrise And Sunset Data Obtained From The App Developed
DATE SUNRISE
(w/).
SUNSET
(w/).
1/10/2018 1538458015 1538501335
2/10/2018 1538458004 1538501355
3/10/2018 1538544396 1538587717
4/10/2018 1538630797 1538674100
5/10/2018 1538717190 1538760467
6/10/2018 1538803582 1538846838
7/10/2018 1538803579 1538846817
8/10/2018 1538976370 1539019567
9/10/2018 1539062768 1539105951
10/10/2018 1539149164 1539192329
11/10/2018 1539235557 1539278690
12/10/2018 1539321956 1539365065
13/10/2018 1539321957 1539365052
14/10/2018 1539494751 1539537815
15/10/2018 1539581150 1539624189
16/10/2018 1539581151 1539624175
17/10/2018 1539753948 1539796943
18/10/2018 1539840347 1539883312
19/10/2018 1539926745 1539969683
20/10/2018 1539926748 153399696
21/10/2018 1540099551 1540142441
22/10/2018 1540185953 1540228825
23/10/2018 1540272354 1540315214
24/10/2018 1540358758 1540401593
25/10/2018 1540445162 154048797
26/10/2018 1540531567 1540574357
27/10/2018 1540617974 1540660734
28/10/2018 1540704378 1540747125
29/10/2018 1540790785 1540833509
30/10/2018 1540877192 1540919896
31/10/2018 1540963600 1541006282
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4.2 DISCUSSION
Base on the graphs which shows the average data for wind speed, for week 1 in the month of July,
the application developed has an average data of 3.73 m/s, while for yandexweather has 3.55 m/s
and 3.52 m/s wind speed for freemeteo. For week 2, application developed has average data of 3.82
m/s, 3.4 m/s for yandexweather and 4.24 m/s wind speed for freemeteo. Week 3, application
developed has an average of 3.52 m/s, 3.38m/s, and 4.19 m/s wind speed for freemeteo. Week 4 has
an average of 3.36m/s wind speed for the application developed, 3.26m/s wind speed for
yandexweather, while 4.95m/s for freemeteo.
In the month of August, the android mobile application developed has an average data of 3.79m/s
wind speed for the first week, 3.28m/s for yandexweather and 4.43m/s wind speed for freemeteo.
For week 2, the mobile app. developed has an average data of 3.33m/s wind speed, 3.3m/s, and
3.84m/s for freemeteo. For week 3, the android mobile application developed has an average data
of 3.49m/s wind speed, 3.34m/s for yandexweather, and 5.74m/s wind speed for freemeteo.
For the last week for month of august, the android app developed has an average data of 3.27m/s,
3.15m/s for yandexweather, and 4.83m/s for wind speed.
For the month of September, in the first week, the app has an average of 3.57m/s wind speed, while
yandexweather has 3.15m/s wind speed, and 4.16m/s for freemeteo. For week 2, the app has
3.07m/s, 3.28m/s for yandexweather, and 5.09m/s wind speed for freemeteo. For week 3, the app
developed has an average data of 2.57 m/s wind speed, yandexweather has 3.21m/s, while
freemeteo has 4.17m/s wind speed. Week 4 has an average data of 2.86m/s wind speed for the
application developed, yandexweather has an average data of 3.35m/s wind speed and 3.85m/s
wind speed for freemeteo.
For October, the application developed has an average number of 2.52m/s wind speed, nimet has
the average data of 1.24m/s wind speed, yandexweather has 3.25m/s, freemeteo has 4.63m/s win
speed. For week 2, app developed has 3.30m/s average wind speed, NIMET has 1.75m/s,
yandexweather has 3.25m/s wind speed, freemeteo has 3.35m/s wind speed. Week 3, the app
developed has 2.48m/s average wind speed, NIMET has the average data of 2.66m/s wind speed,
yandex has 3.54m/s, and freemeteo has 4.75m/s wind speed. Week 4, the app developed has the
average data of 2.69m/s wind speed, NIMET has 1.34m/s, yandexweather has 3.4m/s, and
freemeteo 3.80m/s average data for wind speed.
For the ANOVA Table, The significant value or the statistical difference between the groups
(ABE-Openweathermap, yandexweather, and freemeteo) which is represented as p-value is 105E-
13, which is lower to the alpha level (0.05) for test for differences, and also the f-statistic is higher
than the f-critical, with this, there is a significant difference between the groups and null hypothesis
can be rejected. Therefore, the differences between the mean are statistically significant because the
p-value is less than or equal to the significant level (0.05).
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For correlation Table, the correlation between the obtained data from the application developed
from the registered agriculture api key (openweatherMap) and yandexweather is 0.010827.
Technically there is a positive correlation between the two, the relationship between the variables
are weak.
Also, the correlation between the data obtained from the application developed and the data
obtained from freemeteo is 0.086045. Technically there is a positive correlation between the two.
With all the results shown from Figure 2 to Figure 6 from the graphs above, for the month of July,
there are little variance from the average data gotten for each weeks with the other data gotten from
other automatic weather stations for wind speed.
Same variance applicable to the month of August, September and October, the cause of this little
variance was due to where the automatic weather stations were sited at different locations, i.e. some
automatic weather station were located on hill, some at the river banks, some on snow area, while
some on a dry land, and so on. Therefore the wind speed, the sunrise and sunset data obtain from
different automatic weather station location will be at variance, so for the data obtained from the
weather stations in comparison with the weather information obtained from the application
developed are so close to each other.
The android mobile application developed has performed its function in enhancing precision
farming, thereby providing accurate information which the farmer need on the farm in other to
ensure smart farming.
5. CONCLUSIONS AND RECOMMENDATIONS
5.1 Conclusions
The application that get current weather information basically for wind speed, sunrise and sunset
and map for ado Ekiti in which the average data of the weather information will be obtained has
been developed.
The developed android application is a decision support tool (will give a solution to this problem)
and it is also a current technological advancement free API service and location based mapping
services with low cost sensing capabilities. It will provide correct details for the sunrise and sunset
and wind speed.
The application has been tested on an android mobile device; the application was developed to
work on an android device, which works well on the android phone tested with. The use of this
mobile application will encourage youth to go into agriculture, enhance and boost farmers’ crop
productivity, because this application is a concept of precision farming.
Precision farming is about an approach to farm management which uses information technology,
that gives precise information about what crop or plant needs for its optimum health for better
agricultural production.
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This work shows the significant effect of the development of the android mobile application,
enhancing and encouraging farming in a modernized way because it will make it easier for farmer
to be able to study and know the specific and correct weather data and all other things incorporated
to farming system.
With the use of this application developed, it will allow farmers to make a wise decision on their
farm land, on what to plant, for siting of their poultry house, pig pen and other farm activities
because the two parameters of weather in the case plays significant roles.
5.2 Recommendation
Based on the findings and the results of this project, farmers who face problems on their farm based
on irregular or poor yield of their crops should adopt a precision farming method so as to boost
their crops yield and enable the farmer to make wise decision and engage in smart farming.
This project serves as a basis for which agricultural methods and practices can be enhanced using
computer which is a common, efficient and flexible machine relatively useful in agriculture and
other meteorological related fields, as well as geo-spatial techniques which is widely becoming a
very useful tool in every developing sector.
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