Agro-Genius : Crop Prediction Using Machine Learning

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Volume 4, Issue 10, October – 2019 International Journal of Innovative Science and Research Technology
ISSN No:-2456-2165
IJISRT19OCT1880 www.ijisrt.com 243
Agro-Genius: Crop Prediction using Machine Learning
Thayakaran Selvanayagam1, Suganya S2, Puvipavan Palendrarajah3
Mithun Paresith Manogarathash4, Anjalie Gamage5, Dharshana Kasthurirathna6
Faculty of Computing, Sri Lanka Institute of Information Technology (SLIIT)
Malabe, Sri Lanka.
Abstract:- This paper present a way to aid farmers
focusing on profitable vegetable cultivation in Sri Lanka.
As agriculture creates an economic future for developing
countries, the demand of modern technologies in this
sector is higher. Key technologies used for this problem
are Deep Learning, Machine Learning and Visualization.
As the product, an android mobile application is
developed. In this application the users should input their
location to start the prediction process. Data
preprocessing is started when the location is received to
the system. The collected dataset divided into 3 parts. 80
percent for training, 10 percent for testing and 10 percent
for validation. After that the model is created using LSTM
RNN for vegetable prediction and ARIMA for price
prediction. Finally, for given location profitable crop and
predicted future price of vegetables are shown in the
application. Other than the prediction, optimizing for
multiple crop sowing according to the user requirements
and visualizing cultivation and production data on map
and graphs are also given in the application. This paper
elaborates the procedure of model development, model
training and model testing.
Keywords:- Machine Learning, Android Application, Data
preprocessing, LSTM, RNN, ARIMA, Linear Programming,
Visualization, Polygons.
I. INTRODUCTION
A substantial percentage of the inhabitants of the
country depend on the agriculture. The technological
advancement in agriculture plays an important role in every
farmer’s life to earn good profit. But nowadays percentage of
total GDP has been dropping. In 2005 the agriculture GDP
share was 17.2% but in 2012 it has dropped to 11.1% and
now it is even low [1]. Approximately 80% of the farmers are
from rural areas so if crop production revenue goes down thus
affect their lifestyle because of the industry level farms.
Apparently, Farmers’ experience on the agriculture field
involves in the crop prediction. Farmers who were in the
rustic areas are cultivating according to their personal
experience and knowledge due to absence of reliable and
timely information. Since the modernization occupying the
agriculture field rapidly by the introduction of superior seeds
and different varieties and large number of crops which were
cultivated by agricultural industries, the farmers are forced to
adapt to this hasty change by cultivating more and more
crops. Also, the main issue that small farmers are currently
facing is that, they sow the crops according to their own
experiences. But when they are cultivating and bringing them
to market, they face difficulties to market their product at a
reasonable price. It is because of large farms cultivating the
same. As our country is small, products are distributed all
over the country in between Districts (Dambulla to Jaffna,
Dambulla to Petra, etc.). Because of this, small-scale rural
farmers affected economically.
Nowadays weather condition is not like previous
decades. Day by day it is changing because of the
globalization, so farmers have faced difficulties to predict
weather conditions. They may be some natural disaster which
can also affects cultivation in a sudden. Without the weather,
there are some major factors such as seasonal crop details,
crop combination and suitable crop for given location which
they must have knowledge of these things were gained from
their past experience so without experience they can’t get
expected revenue. By considering these factors Agro-Genius
system is recommended as a solution, hope that it will be very
helpful for farmers to get expected revenue from their
cultivation.
The main research problem is to help small medium
farmers to increase revenue from their cultivation without
getting affected by industrial level farmers and to reduce
surplus marketing. Hitherto in our country there are no
implemented techniques in usage, but agriculture department
keeps so many raw data and using few in their website for
public access, but it is not helpful to farmers. They cultivate
according to their experience. When it’s come to market,
industry level farmers sell their product in a wholesale to all
over the country at the same time rural farmers also bring
their product, but they can’t sell with a reasonable price. In
this situation industry level farmers have no huge loss, but
rural farmers loss their profits and even capital.
The principal scope of this research is; delivering a
mobile application where all type of processing is done in the
cloud-based system through the API calls. Which will be
much helpful for the farmers and industries to select most
profitable crop and its expected price during harvesting time.
Further user can view the currently cultivated crop details in
locations around the country on a map and user is able to
optimize for profitable multiple crops for a specific land. The
following data are collected from the relevant departments
and from other third-party services.

Volume 4, Issue 10, October – 2019 International Journal of Innovative Science and Research Technology
ISSN No:-2456-2165
IJISRT19OCT1880 www.ijisrt.com 244
 Recommended crops details (location wise)
 Recommended crop harvesting duration.
 Past cultivation and production of vegetables.
 Currently cultivated crop which are updated biweekly.
 Weather forecast data.
 Combination of crops which will give more yield.
 Past market prices for each crop.
For the above-mentioned problem, Agro-Genius provide
a solution using above listed past data. As it has more than 10
years of data it is not possible for human to predict from those
huge amounts of data. So, to overcome this challenge
Machine Learning would be more suitable technology now.
In the above listed data, main data like past cultivation and
production of vegetables, weather data and past market price
report are timeseries data. To handle timeseries prediction
some Machine Learning and Deep Learning algorithms are
selected and explained in Methodology section below.
The research reduce problems of rural farmers and it
suggest solution for more profitable cultivation. It helps
farmers to take decisions when starting vegetable farms.
II. RELATED WORK
In past years several systems have been proposed to
implement crop prediction using machine learning techniques
in several countries. Different Machine Learning algorithms
were used for prediction. Multiple Leaner Regression (MLR)
has been applied to predict on past data like year, area of
sowing, rainfall, and yield and Data Mining methodology
(Density – based clustering technique) is used to analyze and
verify the result which was obtained from MLR [2]. On the
other hand, for future forecasting was done by analyzing past
historical price data, climate, location of market and planting
area. Prediction was done for 15 market price data and 100
different crops using different algorithms like ARIMA,
Artificial Neural Network (ANN), Response Surface
Methodology (RSM) and calculate its Mean Absolute
Percentage Error (MAPE). According to the lowest error
percentage, many have selected ANN and PLS as prediction
algorithms [3].
Arun Kumar et al… have proposed system to predict
yield of the crop by analyzing past soil dataset, rainfall
dataset, yield datasets. Prediction was done using K-Nearest
Neighbor and Support Vector Machine algorithm and Least
Squares algorithms [4]. Askunuri Manjula et al… has done
crop prediction using weather forecasting, pesticides and
fertilizers to be used and past revenue as input data.
Multilinear Principal Component Analysis (MPCA) was used
for feature reduction. Optimal Neural Network classifier
(ONN) has been used for data prediction. Other than the
prediction they consider preprocessing and feature reduction
[5].
Leisa et al… have proposed Agriculture decision support
framework for visualization and prediction of western
Australian crop production system which will output
visualizations of seasonal patterns of rainfall for individual
district and show the effect of various scenarios. This system
consists six major components which are data input, data
mining, database, statistical analysis, prediction and
visualization. Data input was done by Graphical User
Interface (GUI). Data visualization done by two methods
which are general trends and spatial interpolation. Data
mining was done by the use of association rules which uses
Apriori algorithms. [6]
There is lack of implemented systems used in other
countries like United State. One is Field Check app which
visualizes currently cultivated crop details in map, but it is
visualizing some selected crops only [7]. Another one is
Descartes Crop app which is forecasting crop yield for
selected area in United States [8]. More over DEKALB is
used to optimize multiple crop combination for small farms.
Other than this implemented application Sri Lankan [9] and
Taiwan [10] agriculture departments maintain some raw data,
which are suitable crop for each land areas, past production
for districts and historical price data. These help farmers, even
though there are no prediction technology. Farmers have to go
through the data one by one to make any decision.
In above mentioned proposed and implemented systems,
they have not considered all the factors which are affecting
farmers in the real world. If they consider main factors, then it
will be more accurate. According to our country many factors
affect farmers profit like weather, past cultivation and
production details, market price etc. but there is no
implemented system to guide Sri Lankan famers, so they
failed to select profitable crop during seasons. In this system
most of the features needed to solve the current problems are
included and help farmers to select profitable crops. The
application will provide predicted results such as most
profitable crop and it’s expected price according to the
location and harvesting time. Also, users can view the
cultivation details visualized in map as it will be more
effective than statistical data.
III. METHODOLOGY
This proposed system contains four main components
such as crop prediction, price prediction, visualization and
optimization. Each component uses different Machine
Learning algorithms and techniques, they are Long-Short
Term Memory (LSTM), Auto Regressive Integrated Moving
Average (ARIMA), Linear Programming and Gastner-
Newman Cartogram techniques to predict and visualize raw
datasets.

Volume 4, Issue 10, October – 2019 International Journal of Innovative Science and Research Technology
ISSN No:-2456-2165
IJISRT19OCT1880 www.ijisrt.com 245
A. Long-Short Term Memory –
LSTM is type of neural network which perform better
result in time series prediction. Purpose of this algorithm is
avoiding the long-term dependency and LSTM is called cell
state which contain different gate. [11]
Fig 1:- LSTM cell state diagram, Image downloaded from
https://commons.wikimedia.org/wiki/File:The_LSTM_cell.png#/media/File:The_LSTM_cell.png
The above diagram shows different notations are used,
where Xt denote input vector, Ht-1 denote Previous cell output,
Ct-1 denote previous cell memory in addition Ht is Current cell
output and Ct denote Current cell Memory. Following
formulas are used to find the values of above-mentioned
notations.
Ft = σ (Xt * Uf + Ht-1 * Wf)
C’ = tanh (Xt * Uc + Ht-1 * Wc)
It = σ (Xt * Ui + Ht-1 * Wi)
Ot = (Xt * Uo + Ht-1 * Wo)
Ct = ft * Ct-1 + It * C’t
Ht = Ot * tanh (Ct)
B. Auto Regressive Integrated Moving Average (ARIMA) –
ARIMA is statistical analysis model that is used for time
series data prediction. ARIMA is divided into 3 components
such as Autoregression (AR), Integrated (I) and Moving
Average (MA). ARIMA model is classified as ARIMA(p,d,q)
where p denotes the number of lag observation in the model,
d denotes the number of times that the raw observations are
differenced and q denotes the moving average window
size[12]. Following formula is used to find the price
forecasting where µ denote constant value.
Ŷt = µ + Ø1 yt-1 + .. + Øp yt-p – Ɵ1 et-1 .. – Ɵq et-q
C. Linear Programming
It is an optimization technique, by using this can find the
optimum points of object function.
D. Gastner-Newman Cartogram
It is a technique for representing data for locations.
Cartogram is a powerful approach to map data [13]. It
provides strong visual for numerical area also this technique
doesn’t need data to be normalized. Comparing other
technique, this is easy to visualize each polygon.
 Used Datasets
This system (Agro-Genius) is fully based on statistical
data and most of the data are from Agriculture Department of
Sri Lanka. There were data collected for more than 10 years
with different seasons in Sri Lanka like Yala and Maha.
Below are the important factors that affect agricultural crop
yield. which were selected for this research.
1. Crop production and extent: crop cultivated area in
hectares and total production in metric ton for every year
in each district in Sri Lanka for two main season such as
Yala and Maha.
2. Recommended crop: each district located with different
soil type therefore each district has recommended crop
according to the soil type.
3. Crop duration: number of days that take to harvest from
seeds for search type of crop.
4. Crop combination: crop type which can be sewed together
in same land.
5. Current cultivation extent: biweekly updated data of
cultivation extent of crops in the present time.
6. Price reports: Crop market price in main markets in
selected districts were taken from Central Bank of Sri
Lanka.
7. Weather data: weather data for coming weeks are
received from AccuWeather.

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ISSN No:-2456-2165
IJISRT19OCT1880 www.ijisrt.com 246
Fig 2:- flow of the system
The following figure shows the workflow of the system.
Location of user will be inputted to the system.
Otherwise user should manually input the location where
he/she wants to get the prediction. In the prediction
component, with the given location the existing trained model
will be analyzed, and it will predict suitable crops and then
the predicted crop will be analyzed with the price prediction
model and expected price will be listed. And then if the user
wants to optimize the crops that were predicted for a better
profitable combination user can proceed to the optimization
component. For more detailed explanation of current cropping
around the country, the current cropping data are mapped in
to the map. Another view can be obtained to visualize the past
cropping pattern.
i. Crop & Price Prediction
To maximize crop profit, appropriate crop selection will
play a vital role. In this paper profitable crop selection based
on statistical data like past production data, recommended
crop details for each district, past price data and weather
forecasting data are used. To analyze these data RNN &
LSTM technique was used. After crop selection, for those
selected crops expected price in harvesting time will be
predict using ARIMA technique.

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ISSN No:-2456-2165
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ii. Visualization
Currently cultivated crop details are displayed in Sri
Lankan map with different colors using Gatogram technique
& past cultivation details will be visualized using a bar chart.
iii. Multi Crop Optimization
Optimize for profitable crop combinations using linear
programming for the predicted crops with optimization inputs
like suitable crop combination.
IV. RESULTS & DISCUSSIONS
As mentioned, above this system contains four
component all component depends on each other because
output of the one component will be the input of another
component so the accuracy of each component will affect the
entire system, And this is fully based on dataset prediction, so
accuracy is very important, Since this system is fully based on
dataset all collected raw datasets are normalized according to
the model requirement within that normalized data 80% of the
data was taken as training data, 10% taken for validating and
10% taken for testing.
A. Crop Prediction
This prediction involves many datasets. All datasets are
preprocessed according to the user location and trained using
LSTM & Random Forest Regression model. For the
comparison, the districts where the user’s market is
considered. Here past data set of production for last 10 years
for each district were used. LSTM gives more accuracy for
this time series data still current data amount is not enough for
LSTM to give higher accuracy as it has only seasonal
cultivation and production. So, Random forest is working
better for this dataset.
Fig 3:- Normalized cultivation & production data
Fig 4:- Normalized price data
B. Crop Price Prediction
Price data for last 10 years were used to predict expected
price for each crop. This price differs from each district, so
the location was considered as well. Price data filtered by
location and those filtered data were processed using ARIMA
and LSTM models and Mean Absolute Percentage Error was
calculated. Since these price datasets is time series and huge
so ARIMA is the best selection for price prediction and
accuracy also higher than LSTM. Result of each algorithm are
shown below.
Fig 5:- Predicted price forecasting using ARIMA model
Fig 6:- Predicted price forecasting using LSTM model
C. Visualization
Currently cultivated data were analyzed and visualized
in the Sri Lankan map using Cartogram technique. Which
locate the cultivated geographical location as per the latitude
and longitude value. Mainly 6 districts were considered in Sri
Lanka (Matale, Kandy, Nuwara Eliya, Jaffna, Kilinochchi,
Mullaitivu) Within those districts cultivated areas and it’s
details like cultivated area in hectare and harvesting time were
taken. This visualized map will be updated according to the
harvesting time.

Volume 4, Issue 10, October – 2019 International Journal of Innovative Science and Research Technology
ISSN No:-2456-2165
IJISRT19OCT1880 www.ijisrt.com 248
Fig 7:- Visualized currently cultivated data
Also, past statistical results analyzed would be
visualized using bar graph that indicate previously cultivated
details in hectares according to the district for each crop.
D. Multi Crop Optimization
Linear programming model was used with input
parameters such as predicted crop list, crop suitability, area in
hectare and no of crops that user want to cultivate. Which
give 89.66% accuracy for prediction with the available data
set.
V. CONCLUSION & FUTURE WORK
Agriculture is the major economic force in Sri Lanka. It
has moderate climate throughout the year in most parts of the
country [14]. As the country is small, cultivated crops are
distributed all over the country, because of that a reasonable
market price is remaining as a challenging issue for farmers.
To overcome this problem, Agro-genius application advice to
predict the most profitable crops and its expected price during
harvesting time according to the location, by predicting
different historical raw datasets using different machine
learning algorithms like LSTM & RNN, ARIMA, Linear
Programming (LP), Gastner Newman Cartogram algorithm
etc. Also, currently cultivated crop details are visualized in a
map with details, which will help farmers to view the nearby
district cultivation details. This system helps farmers to take
correct decision for selecting suitable crops, which will
maximize the profit.
Fig 8:- Past production details
Fig 9:- Past cultivation details
This system considers main features which impact the
profit by taking data in six districts. In future the system can
be expanded by considering more features like soil type and
water level and so on. Also expand to provide fertilization
calendar and guidelines which will helps farmers who have no
experience about crops. Also, in the system can be modified
to receive data from IoT devices without depending on raw
data. Other than that, this system can be developed for other
platforms as well.

Volume 4, Issue 10, October – 2019 International Journal of Innovative Science and Research Technology
ISSN No:-2456-2165
IJISRT19OCT1880 www.ijisrt.com 249
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