DATA MINING TRENDS IN AGRICULTURE : A REVIEW

Abstract

Data mining in agriculture is a relatively novel research field. Agriculture data are highly diversified in terms of nature, interdependency and use of resources for farming. The major problem of using data mining in agriculture is that to solve issues based on the available data and its meaningful outcomes. In data mining, clustering and classification technique make ingenious information in research and knowledge acquisition from integrated farming. And that producesbetter solution for the farmers about their cultivation (yield). Forthcoming data mining in agriculture rising research field in crop yield analysis. Different data mining techniques such as K-Means, K-Nearest Neighbor (KNN), Artificial Neural Networks (ANN) and Support Vector Machines (SVM) are used for new application research in agriculture field. In this paper, the techniques of predicting yield production of a crop are the centre of focus. Yield prediction is very important in agriculture. The problemof yield prediction can be solved by utilizing data mining techniques.

Full text

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DATA MINING TRENDS IN AGRICULTURE : A REVIEW
*PATEL, AMIKSHA A.1 AND KATHIRIYA, DHAVAL R.2
CENTER FOR AGRICULTURAL INFORMATION AND COMMUNICATION
TECHNOLOGY
SARDARKRUSHINAGAR DANTIWADA AGRICULTURAL UNIVERSITY
SARDARKRUSHINAGAR – 385 506, GUJARAT, INDIA
*E-MAIL: amiksha_patel@yahoo.com
______________________________________________________________________________
1.Assistant Professor (Computer Science), Centre for Agricultural Information & Computer Technology, Sardarkrushinagar
Dantiwada Agricultural University, Sardarkrushinagar – 385506 Dist. Banaskantha, Gujarat.
2.Director, Information Technology, Anand Agricultural University, Anand, Gujarat
ABSTRACT
Data mining in agriculture is a relatively novel research field. Agriculture data are
highly diversified in terms of nature, interdependency and use of resources for farming. The
major problem of using data mining in agriculture is that to solve issues based on the
available data and its meaningful outcomes. In data mining, clustering and classification
technique make ingenious information in research and knowledge acquisition from integrated
farming. And that produces better solution for the farmers about their cultivation (yield).
Forthcoming data mining in agriculture rising research field in crop yield analysis. Different
data mining techniques such as K-Means, K-Nearest Neighbor (KNN), Artificial Neural
Networks (ANN) and Support Vector Machines (SVM) are used for new application research
in agriculture field. In this paper, the techniques of predicting yield production of a crop are
the centre of focus. Yield prediction is very important in agriculture. The problem of yield
prediction can be solved by utilizing data mining techniques.
KEY WORDS: Artificial Neural Networks, Data mining, K-Means, K-Nearest Neighbour,
Support Vector Machines
INTRODUCTION
The Indian agriculture is highly
differentiated in terms of its climate, soil,
water, crops, horticultural crops, plantation
crops, medicinal crops, livestock, etc.
Today, India ranks second worldwide in
farm output. Agriculture is facing the
problem of changing in the resources that
are directly affecting to the crop yield, so the
agricultural productivities in India are
unpredictable. For balanced and sustainable
growth of agriculture, these resources need
to be evaluated, monitored and analysed, so
that proper methods can construct.
Accurate and reliable information
about crop yield prediction is important for
taking decisions for agricultural risk
management. Crop yield prediction is also
important for supply chain operation of
companies engaged in industries that use
agricultural produce as raw material
(Gleason, 1982). Livestock, food, animal
feed, chemical, poultry, fertilizer, pesticides,
seed, paper and many other industries uses
agricultural products as intergradient in their
production processes. An accurate estimate
of crop yield helps these companies in
planning supply chain decision like

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production scheduling and it is useful for
business such as seed, fertilizer,
agrochemical and agricultural machinery
industries and marketing activities based on
crop yield.
Data mining techniques till now used
widely in business and corporate sectors
may be used in agriculture for data
characterization, discrimination and
predictive and forecasting purposes. Some
use of data mining in soil characteristic
evaluation has already been attempted.
Different techniques have been
intended for mining data over the years. The
most used data mining techniques are
discussed in this paper. The application of
data mining techniques like k-means, bi
clustering, k nearest neighbor, Artificial
Neural Networks, Support Vector Machine
and Naïve Bayes Classifier in the agriculture
field. Data mining techniques can be,
therefore, grouped in two different ways. An
analysis of the results of crop cutting
experiments in agriculture for yield of
various crops (Deshpande, 2003). They can
be clustering or classification techniques.
Additionally, some of them provides a list of
information for clustering or classification
purpose, while other learns from the
available data for how to perform
classifications.
So we can say that “Data mining is
an expressively production of previously
unrevealed set of records to probably useful
and materialistic data. It is the process of
examining data from different perceptions
and summarizing it into useful information.”
Data mining techniques
Data mining techniques can be
divided in two groups: Classification and
Clustering techniques. Classification
techniques are designed for classifying
unknown samples using information
provided by a set of classified samples. This
set is usually referred to as a training set,
because, in general, it is used to train the
classification technique how to perform its
classification. For example, Neural
Networks and Support Vector Machines
exploit training sets for tuning their
parameters in order to solve a particular
classification problem. In other words, these
two classification techniques learn from a
training set how to classify unknown
samples. Another classification technique,
the k nearest neighbor does not have any
learning phase, because it uses the training
set, every time a classification must be
performed. In the event, a training set is not
available, there is no previous knowledge
about the data to classify. In this case,
clustering technique can be used to split a
set of unknown samples into cluster. One of
the most used clustering techniques is the k–
means method. This paper mainly focuses
on the most used techniques in agriculture
related fields.
The main techniques for data mining
include association rules, classification,
clustering and regression. The different data
mining techniques used for solving different
agricultural problem has been discussed
(Mucherino et al. 2009).
Association rules
Association rules mining technique
is one of the most efficient techniques of
data mining to search unseen or desired
pattern among the vast amount of data. In
this method, the focus is on finding
relationships between the different items in a
transactional database. Association rules are
used to find out elements that co-occur
repeatedly within a dataset consisting of
many independent selections of elements
(such as purchasing transactions), and to
discover rules.
The simple problem statement is:
Given a set of transactions, where each
transaction is a set of literals, an association
rule is a phrase of the form A = > B, where
A, B are sets of objects. The instinctive

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meaning of such a rule is that transactions of
the database which contain A tend to contain
B. (Srikant et al., 1997). An application of
the association rules mining is the market
basket analysis, customer segmentation,
store layout, catalog design and
telecommunication alarm prediction. The
different association rules mining algorithm
are Apriori Algorithm (AA), Partition,
Dynamic Hashing and Pruning (DHP),
Dynamic Itemset Counting (DIC), FP
Growth (FPG), SEAR, Spear, Eclatand
Declat, MaxEclat, etc. (Zaki, 1999).
Classification
Classification and prediction are two
forms of data analysis that can be used to
extract models describing important data
classes or to predict future data trends. It is a
process in which a model learns to predict a
class label from a set of training data which
can then be used to predict discrete class
labels on new samples. To maximize the
predictive accuracy obtained by the
classification model when classifying
examples in the test set unseen during
training is one of the major goals of
classification algorithm. Data mining
classification algorithms can follow three
different learning approaches: semi-
supervised learning, supervised learning and
unsupervised learning. The different
classification techniques for discovering
knowledge are Rule Based Classifiers,
Bayesian Networks (BN), Decision Tree
(DT), Nearest Neighbour (NN), Artificial
Neural Network (ANN), Support Vector
Machine (SVM), Rough Sets, Fuzzy Logic,
Genetic Algorithms, etc. (Beniwal and
Arora, 2012).
Clustering
In clustering, the focus is on finding
a partition of data records into clusters such
that the points within each cluster are close
to one another. Clustering groups the data
instances into subsets in such a manner that
similar instances are assembled together,
while dissimilar instances belong to diverse
groups. Since the aim of clustering is to find
out a new set of categories, the latest groups
are of interest in themselves, and their
assessment is intrinsic (Xu and Wunsch,
2005). There is no prior knowledge about
data. The different clustering methods are
Hierarchical Methods (HM), Partitioning
Methods (PM), Density-based Methods
(DBM), Model-based Clustering Methods
(MBCM), Grid-based Methods and Soft-
computing Methods [fuzzy, neural network
based], Squared Error-Based Clustering
(Vector Quantization), network data and
Clustering graph (Fayyad et al., 1996).
Regression
Regression is learning a function that
maps a data item to a real-valued prediction
variable. The different applications of
regression are predicting the amount of
biomass present in a forest, estimating the
probability of patient will survive or not on
the set of his diagnostic tests, predicting
consumer demand for a new product
(Sawaitul et al., 2012). Here the model is
trained to predict a continuous target.
Regression tasks are often treated as
classification tasks with quantitative class
tag. The methods for prediction are
Nonlinear Regression (NLR) and Linear
Regression (LR).
Regression analysis (Sellam and
Poovammal, 2016), linear are cited.
Described about various environmental
factors that influence the crop yield and the
relationship among these parameters is also
established.
Applications of data mining techniques in
agriculture
There are many studies which have
been accepted on the application of data
mining techniques for agricultural data sets.
Naive Bayes data mining technique is used
to classify soils that analyse large soil
profile experimental data sets. Decision tree
algorithm in data mining is used for

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predicting soil fertility. By using clustering
techniques based on Partitioning Algorithms
and Hierarchical Algorithm, the land
utilization for agriculture and non-
agriculture areas for the past ten years has
been determined.
As early into the growing season as
possible, a farmer is always concerned with
how much yield of his crop. In the past, this
yield prediction has been relied on farmer’s
experience for particular yield, crops and
climatic conditions. However, this
knowledge might also be available, but not
exactly for the small scale. Accurate data
which can collect in seasons using a
multitude of seasons. Advancement and
consistence of the agricultural production at
a faster in time is one of the basic necessary
for agricultural development.
In India, area and yield production of
different crops are the results, and reflection
of the combined effect of many factors, like
agro-climatic conditions, resource
endowment, technology level, infrastructure,
social and economic conditions. Many
schemes have been invented to maximize
the productivity of various crops in different
agro-climate region, institution, seed,
fertilizer, pesticide, fungicide companies and
many other activities are actively engaged in
the productivity of different crops in
different regions and under different
condition (Just and Weinenger, 1999;
Veenadhari et al., 2011).
Now-a-days, IT had grown to be
more and more part of our day by day
activities. With IT developments in
efficiency can be made in almost any part of
industry and services, and now this is true
for agriculture. A farmer not harvests only
crops but also emerging quantity of data.
These data are accurate and in very less
amount. There is a lot of data available
having information about agriculture. Here
soil and yield assets that should be useful in
a way that farmers are beneficial. This is a
general problem for which data mining is
been there. Data mining techniques aim at
finding that information in the data that are
both important and beneficial to the farmer.
A common particular problem of farmers is
yield prediction.
Neural networks
Sawaitul et al. (2012) focuses the
information about weather. The recorded
parameters are used to forecast weather. If
there is a change in any one of the recorded
parameters like wind speed, wind direction,
temperature, rainfall, humidity, then the
upcoming climatic condition can be
predicted using artificial neural networks,
back propagation techniques. The increase
in signal range will work in large areas as
well. Neural network models for
predicting flowering and physiological
maturity of soybean (Elizondo et al., 1994).
Somvanshi et al. (2006) deliberated the
modelling and prediction of rainfall using
artificial neural networks and Box- Jenkins
methodology along with other applications
of artificial neural networks in hydrology is
forecasting daily water hassle and flow
forecasting.
Maier and Dandy (2000) used BP
neural network and simulated the result
using MATLAB. They found suitable data
model for achieving high accuracy for price
prediction. The prediction is mainly based
on only price. Neural networks for the
prediction and forecasting of water
resources variables.
K-means
Data mining is the process of
discovering meaningful patterns and trends
by shifting through huge amount of data,
using pattern detection technologies as well
as statistical and mathematical techniques.
Data mining techniques are often used to
study soil characteristics. As an example, the
K-Mean approach is used for classifying

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soils in combination with GPS based
techniques (Verheyen et al., 2001).
Urtubia et al. (2007) stated that the
prediction of wine fermentation problems
can be performed by using a k-means
approach. Knowing in advance that the wine
fermentation process could get jammed or
be slow can help the enologist to correct it
and ensure a good fermentation process. The
K-Means algorithm is used in performing
atmosphere pollution forecast (Jorquera et
al., 2001). Verheyen et al. (2001) used K-
Means approach to classify soils and plants
and Camps-Valls et al. (2003) used SVMs to
classify crops. Apples are checked using
different approaches before sending them to
the market (Breiman et al., 1984).
Fathima, G. N. and Geetha (2014)
used k means and Appriori algorithm, crop
type and irrigation parameters and focused
on the policies that government could frame
by the cropping practices of farmers.
Fuzzy set
Jagielska et al. (1999) described the
applications to agricultural related areas
such as yield prediction is a very important
agricultural problem. Any farmer might be
interested in knowing how much yield is
expected. In the past, yield prediction was
achieved by considering farmer's experience
on particular field, crop and climate
condition. They have discussed additional
information about data like probability in
probability theory, grade of membership in
fuzzy set theory.
Tellaeche et al. (2007) summarized
an automatic computer vision system for the
detection and differential spraying of Avena
sterilis, a toxic weed growing in cereal
crops. With such purpose, it have been
designed a hybrid decision making system
based on the Bayesian and Fuzzy k-Means
classifiers, where the a priori probability
required by the Bayes framework is supplied
by the Fuzzy k-Means. To classifying plant,
soil and residue regions of interest from
colour images using fuzzy clusters (Meyer et
al., 2004).
Naive Bayes, J 48, random forests,
support vector machines, artificial neural
networks were implemented (Sujatha and
Isakki, 2016). Bhargavi and Jyothi (2009)
used climate data and crop parameters for
crop yield prediction. Predicted yield using
Naive Bayes, Apriorityz algorithm, the main
focus was on various soil parameters like
pH, nitrogen, moisture, etc. and comparison
accuracy is also presented. Only 77 per cent
of accuracy is achieved (Hemageetha,
2016).
Decision tree and Bayesian classification
Veenadhari (2007) considered the
influence of climatic factors on major kharif
and rabi crops production in Bhopal District
of Madhya Pradesh State. The findings of
the study revealed that the decision tree
analysis indicated that the productivity of
soybean crop was mostly influenced by
comparative humidity followed by
temperature and rainfall. The decision tree
analysis shows that the productivity of
paddy crop was mostly inclined by rainfall
followed by comparative evaporation and
humidity. For wheat crop, the analysis
showed that the productivity is mostly
influenced by temperature followed by
relative humidity and rainfall. The results of
decision tree were confirmed from Bayesian
classification. The rules formed from the
decision tree are useful for identifying the
conditions intended for high or low crop
productivity.
Shalvi and De Claris (1998) stated
that Bayesian network is a powerful tool and
broadly used in agriculture datasets. The
model developed for agriculture application
based on the Bayesian network learning
method. The results showed that Bayesian
Networks are feasible and efficient.
Bayesian approach improves hydro
geological site characterization even when
using low-resolution resistivity surveys.

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K-nearest neighbour
The k-nearest neighbor classification
algorithmic rule may be divided into two
phases: coaching section and testing section.
Bermejo associated Cabestany urged a
reconciling learning algorithmic rule to
permit fewer information points to be
utilized in coaching information set. Several
different techniques are projected to scale
back procedure burden of k-nearest
neighbour algorithms (Chinchulunn et al.,
2010). A number of studies have been
carried out on the application of data mining
techniques for agricultural data sets. For
example, the K-Nearest Neighbor is applied
for simulating daily precipitations and other
weather variables (Rajagopalan and Lall,
1999). K-Nearest Neighbor approach was
used to analyse and estimate forest variables
for analyzing satellite imagery (Holmgren
and Thuresson, 1998). A K–Nearest-
Neighbor approach is simulator for daily
precipitation and other weather variables
(Rajagopalan, and Lall, 1999).
Support vector machine
The main plan of Support Vector
Machine (SVM) is to classify information
samples into two disjoint categories. The
essential plan behind is classifying the
sample information into linearly severable.
Support Vector Machine (SVM) area unit a
group of connected supervised learning
ways used for classification and regression
(Veenadhari et al., 2011).
The SVM-based data mining is
applied to future climate predictions from
the second generation Coupled Global
Climate Model (CGCM2) to obtain future
projections of precipitation. The results are
then analysed to assess the crash of climate
change on rainfall over India. It is shown
that SVMs provide a promising alternative
to conventional artificial neural networks for
statistical downscaling and are appropriate
for conducting climate impact studies
(Tripathi et al., 2006).
Various changes of the weather
scenarios are analysed using SVMs. Data
mining techniques are also applied to study
sound recognition problems. Fagerlund
(2007) used SVMs for classification of the
sound of birds and other different sounds.
Camps-Valls et al. (2003) used Support
vector machines for crop classification using
hyper spectral data in Pattern recognition
and image analysis.
In the field of Agriculture two or more
Data Mining techniques can be applied.
Some are related to weather conditions or
forecasts
To predict the rainfall, used data
mining over two techniques and compares
yield prediction based on rainfall between
MLR Technique and K-Means. The
estimation of average production was 98 per
cent using MLR Technique and 96 per cent
using K-Means algorithm was given as
accuracy (Ramesh and Vishnu Vardhan,
2013). In the agriculture k-means, ID3
algorithms, the k nearest neighbor, support
vector machines, artificial neural networks
presented the purpose of data mining
techniques and were detailed discussed
(Veenadhari et al., 2014).
For weather forecasting, Bendre et
al. (2015) used Map Reduce and Linear
Regression algorithm. The effective model
to improve the accuracy of rainfall
forecasting is investigated. The forecasting
is done based on only a weather data.
CONCLUSION
Agriculture is the most important
application area mainly in the developing
countries like India. Use of information
technology in agriculture can change the
condition of decision making and farmers
can yield in better way. Data mining plays a
crucial role for decision making on several
issues related to agriculture field. It

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discusses about the role of data mining in
the agriculture field and their related work
by several authors in context to agriculture
domain. There are growing applications for
data mining techniques in agriculture. This
is relatively a new research field and it is
expected to grow in the future. Using data
mining techniques in agriculture can take a
revolution the current condition of decision
making and farmers yield in an advanced
way. Several data mining techniques related
to agriculture domain is useful for
researchers to get information of current
scenario of data mining techniques and
applications in context to agriculture field.
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[MS received : September 22, 2017] [MS accepted : October 07, 2017]