DEVELOPMENT OF AN AGRO-METEOROLOGICAL MONITORING … · 2019-04-02 · from different key sciences such as soil physics and chemistry, hydrology, meteorology, crop and animal and

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ISSN: 2313-3759 www.ijsrit.com

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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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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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

GOOGLE

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

61

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


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


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


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

70


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

71


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).

72


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.

73


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.

74


REFERENCE

Akerele F. O., Omotayo K. F., Ogunlola O. O. and Omotayo T. D. (2016). Computer

Aided Software for Gumbel Model Application for Predicting Meteorological Data

in Agriculture: Case Study. Journal of Emerging Trends in Engineering and Applied

Science (JETEAS) 7(4): 186-195. Scholarlink Research Institute Journals, 2016

(ISSN: 2141-7016). Jetease.scholarlinkresearch.com

"Accessory Development Kit | Android Developers" .Developer.android.com. Retrieved

October 2, 2012.

https://en.m.wikipedia.org/wiki/Precision_agriculture

https://en.wikipedia.org/wiki/Anemometer

https://soilsmatter.wordpress.com/2015/02/27/what-is-precision-agriculture-and-why-is-it-

important/

https://whatis.techtarget.com/definition/precision-agriculture-precision-farming

https://code.tutsplus.com/tutorials/create-a-weather-app-on-android--cms-21587

https://developer.android.com/ndk/guides/stable_apis

https://developer.android.com/ndk/guides/simpleperf

https://www.javaworld.com/article/3095406/android/android-studio-for-beginners-part-1-

installation-and-setup.html By Jeff Friesen

https://www.javaworld.com/article/3104621/mobile-java/android-studio-for-beginners-part-2-e

explore-and-code-the-app.html

https://www.programiz.com/java-programming

https://javatutorial.net/java-eclipse-tutorial

https://blog.teamtreehouse.com/java-basics-for-android-development-part-1

"IntelliJ Android Development" . Retrieved September 19, 2012.

"SDK Tools | Android Developers" Developer.android.com. Retrieved April 25, 2018.

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DEVELOPMENT OF AN AGRO-METEOROLOGICAL MONITORING … · 2019-04-02 · from different key sciences such as soil physics and chemistry, hydrology, meteorology, crop and animal and