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International Journal of Emerging Technology and Advanced Engineering
Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 2, Issue 12, December 2012)
Study and Analysis of Decision Tree Based Irrigation Methods
in Agriculture System
Ravindra M1, V. Lokesha2, Prasanna Kumara3, Alok Ranjan4
1
Research Scholar, Dept. of Computer Scince, Singhania University, Rajasthan – INDIA
2
Acharya Institute of Technology, Bangalore – INDIA
3
Asst. Professor, Dept. of MCA, Cambridge Institute of Technology, Bangalore – INDIA
4
Asst. Professor, Dept. of CSE, Canara Engg. College, Mangalore – INDIA
Data Mining is also known as Knowledge Extraction,
Data/Pattern Analysis, Data Archeology, Data Dredging,
Information Harvesting, Business Intelligence etc. Data
Mining is not specific to one kind of data or media; instead,
it is applicable to any kind of information in the repository
[4]. Its fundamental objective is to provide insight and
understanding about the structure of the data and its
important features, and to discover and extract patterns
contained in the data set. Data mining brings together a
multitude of disciplines, such as database systems,
statistics, artificial intelligence, data visualization, and
others. The discovered knowledge can be applied to
Information Management, Query Processing, DecisionMaking, Process Control and many other applications [5].
Abstract - Most of the energy coupling materials currently
available have been around for decades, their use for the
specific purpose of power harvesting has not been thoroughly
examined until recently, when the power requirements of
many electronic devices has reduced drastically. The objective
of this research is to focus on the power source characteristics
of various transducer devices in order to find some basic way
to compare the relative energy densities of each type of device
and, where possible, the comparative energy densities within
subcategories of harvesting techniques. The successful
implementation of decision tree helps to select the optimized
utilization of the available methods in the irrigation to the
fields to help farmer’s in making the decision in selecting the
best suited pump set for irrigation. The various parameters
such as irrigation types, the area coverage of the field in terms
of acres, capacity of the motor being used for pumping the
water, the height at which the water is being pumped etc., are
considered while making the decision in the selection of best
suited pump set for the irrigation.
II. DATA M INING P ROCESS
Data Mining is a convergence of three key technologies
i.e. Increasing Computing Power, Statistical and Learning
Algorithms, and Improved Data Collection and
Management. The idea of Data Mining is drawn from
Artificial Intelligence, Machine Learning, Pattern
Recognition, Statistics and Database Systems [5]. The main
techniques of data mining are Association Analysis,
Clustering Analysis, Classification, Prediction, Time-Series
Patterns and Bias Analysis.
The steps in the data mining process are: i. Problem
Definition: Defining the business problem is the first and
the most important step in the Data Mining Process., ii.
Data Collection and Enhancement: Involves the steps such
as a. Define Data-Sources, b. Join and Deformalize-Data, c.
Enrich Data, d. Transform-Data iii. Modeling Strategies:
Fall into two categories: supervised learning and
unsupervised learning, iv. Training, Validation, and Testing
of Models: Partitioning data sets into one set of data used to
train a model, another data set used to validate the model,
and a third used to test the trained and validated model, v.
Analyzing Results: Diagnosing and evaluating the results
obtained from a Data Mining Model, vi. Modeling
Iterations vii. Implementing Results.
Keywords- Classification, Decision Trees, Data Mining
techniques, irrigation system, harvesting techniques.
I. INTRODUCTION
The development of Information Technology lead to a
large amount of data generation, and are stored in the
repositories, thus most of the organizations become „data
rich and information poor‟.[1] The conversion of huge
volume of data into highly valued information, which are
used to aid in decision making in various business
processes. The extraction of knowledge from large sets of
data help the business organizations to focus on most
important information‟s in their data warehouses.[2] Data
mining is the science of extracting useful information from
large data sets or databases. The different kinds of
information which could be mined are: i) Business
Transactions Data, ii) Scientific Data, iii) Media and
Personal Data, iv) Surveillance video and images, v)
Spatial data, vi) World Wide Web repositories etc.[3] The
Data Mining techniques are the result of long process of
research and product development.
167
International Journal of Emerging Technology and Advanced Engineering
Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 2, Issue 12, December 2012)
Test metrics are used to assess how accurately the model
predicts the known values. If the model performs well and
meets the business requirements, it can then be applied to
new data to predict the future. Since the classification
model uses the Decision Tree algorithm, rules are
generated with the predictions and probabilities.
III. C LASSIFICATION
Classification is a data mining function that assigns
items in a collection to target categories or classes. In data
mining, classification is one of the most important tasks. It
maps the data in to predefined targets. It is a supervised
learning as targets are predefined. The aim of the
classification is to build a classifier based on some cases
with some attributes to describe the objects or one attribute
to describe the group of the objects. Then, the classifier is
used to predict the group attributes of new cases from the
domain based on the values of other attributes [6]. The
goal of classification is to accurately predict the target class
for each case in the data. Classification technique is
capable of processing a wider variety of data than
regression and is growing in popularity.
The classification task begins with a data set in which
the class assignments are known. In the model build
(training) process, a classification algorithm finds
relationships between the values of the predictors and the
values of the target. Different classification algorithms use
different techniques for finding relationships. These
relationships are summarized in a model, which can then be
applied to a different data set in which the class
assignments are unknown. Classification models are tested
by comparing the predicted values to known target values
in a set of test data. The historical data for a classification
project is typically divided into two data sets: one for
building the model; the other for testing the model. Scoring
a classification model results in class assignments and
probabilities for each case.[7]
The basic classification techniques are: i. Decision Tree
Based Methods, ii. Rule-Based Methods, iii. Neural
Networks, iv. Naïve Bayes and Bayesian Belief Networks,
v. Support Vector Machines etc. The Classification
methods are used in many applications such as customer
segmentation, business modeling, marketing, credit
analysis, and biomedical and drug response modeling etc.
A classification model is tested by applying it to test data
with known target values and comparing the predicted
values with the known values. The test data must be
compatible with the data used to build the model and must
be prepared in the same way that the build data was
prepared. Typically the build data and test data come from
the same historical data set. A percentage of the records are
used to build the model; the remaining records are used to
test the model.
IV. DECISION T REES
Most Data Mining techniques are based on inductive
learning, where a model is constructed explicitly or
implicitly by generalizing from a sufficient number of
training examples. Traditionally, data collection was
regarded as one of the most important stages in data
analysis. The number of variables selected was usually
small and the collection of their values could be done
manually.
In the case of computer-aided analysis, the analyst had to
enter the collected data into a statistical computer package
or an electronic spreadsheet. Due to the high cost of data
collection, people learned to make decisions based on
limited information. Since the dawn of the Information
Age, accumulating data has become easier and storing it is
inexpensive [8]. It has been estimated that the amount of
stored information doubles every twenty months
unfortunately, as the amount of machine-readable
information increases; the ability to understand and make
use of it does not keep pace with its growth.
Training Set
Tree
Induction
Algorithm
Induction
Learn
Model
Deduction
Model
Test Set
Apply
Model
Figure 1: Decision Tree Model
168
Decision
Tree
International Journal of Emerging Technology and Advanced Engineering
Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 2, Issue 12, December 2012)
There are two main types of data mining: VerificationOriented (the system verifies the user‟s hypothesis) and
Discovery-Oriented (the system finds new rules and
patterns autonomously). Each type has its own
methodology. In data mining, a Decision Tree is a
predictive model which can be used to represent both
Classifiers and Regression models. In Operations Research,
on the other hand, decision trees refer to a hierarchical
model of decisions and their consequences. The decision
maker employs decision trees to identify the strategy most
likely to reach her goal. When a decision tree is used for
classification tasks, it is more appropriately referred to as a
Classification Tree. When it is used for regression tasks, it
is called Regression Tree.
Classification Trees are used to classify an object or an
instance to a predefined set of classes (such as risky/nonrisky) based on their attributes values. Classification Trees
are frequently used in applied fields such as finance,
marketing, engineering and medicine. Classification trees
are usually represented graphically as hierarchical
structures, making them easier to interpret than other
techniques. The use of a decision tree is a very popular
technique in data mining. In the opinion of many
researchers, decision trees are popular due to their
simplicity and transparency.
Decision trees (DTs) are either univariate or multivariate
[9]. Univariate Decision Trees (UDTs) approximate the
underlying distribution by partitioning the feature space
recursively with axis parallel hyperplanes. The underlying
function, or relationship between inputs and outputs, is
approximated by a synthesis of the hyper-rectangles
generated from the partitions. Multivariate Decision Trees
(MDTs) have more complicated partitioning methodologies
and are computationally more expensive than UDTs [10].
In the process of determining the right choice for the
harvesting for the farmers, the various parameters such as
irrigation types, the area coverage of the field in terms of
acres, capacity of the motor being used for pumping the
water, the height at which the water is being pumped etc.,
The following points are to be considered during are
considered while making the decision in the selection of
best suited pump set for the irrigation: a. Selection of
Power Supply: i. Single Phase ii. Three Phase, b. Area
available for irrigation, iii. Head to lift water supply, iv.
Additional head for sprinkler irrigation. v. Type of
cultivation, vi. Source of water (Plenty or Limited). vii.
Economy of the power (to decide the cost method of
irrigation), viii. Distance from the transformer, ix.
Type of the Pump set: a). Mono block, b). Submersible,
c) New Open Source, x. Required season for irrigation etc.
I
S
D
H
Q
P
L
H
Q
P
Q
Q
L
P
L
P
L
Figure 2: Decision Tree
I-Irrigation, S-Sprinkler, D-Drip, H-Height, Q-Quantity,
P-Plenty, L-Limited
Tree Classification Rules are :
i. Type(Drip)^Distance(<100)^Height(<50)^Quantity
(Limited) ^ Acres(<5) => 2HP Good
ii. Type(Drip)^Distance(>100)^Height(<50)^Quantity
(Plenty)^Acres(<10) => 5HP Good
iii. Type(Sprinkler)^Distance(<100)^Height(<100)^Quant
ity(Limited)^Acres(<10) => 5HP Good
iv. Type(Sprinkler)^Distance(>100)^Height(<50)^Quantit
y(Plenty)^Acres(<5) => 2HP Good.
The Information Gain for the Decision Tree is calculated
using the formula I=(Log 2 N= log N/log 2). So the
information gain for the left and right hand side of the tree
is:
 P=35, N=29,
I(P,N) = -(35/(35+29))log2 (35/(35+29)) (29/(35+29))log 2 (29/(35+29))
= -0.547 log 2 (0.547) - 0.453 log 2 (0.453)
= -0.547 * -0.87 - 0.453 * -1.142
= 0.476+0.517 = 0.993.
 (Left) P=18, N=14,
I(PSub1,NSub1) = -(18/(18+14))log2 (18/(18+14)) = (14/(18+14))log 2 (14/(18+14))
= -0.56 log 2 (0.56) - 0.44 log 2 (0.44)
= -0.56 * -0.837 - 0.44 * -1.18
= 0.469+0.5192 = 0.9882
169
International Journal of Emerging Technology and Advanced Engineering
Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 2, Issue 12, December 2012)
 (Right) : P=17, N=15,
I(PSub1,NSub1)= -(17/(17+15))log2 (17/(17+15)) (15/(17+15))log2 (15/(17+15))
= -0.53 log 2 (0.53) - 0.47 log 2 (0.47)
= -0.53 * -0.92 - 0.47 * -1.089
= 0.4876+0.512 = 0.9996
E(Type) = ((18+14)/64) * 0.9882 + ((17+15)/64) *
0.9996
= 0.5 * 0.9882 + 0.5 * 0.9996
= 0.4943 + 0.4998 = 0.9941
Therefore the Information Gain for the Irrigation Type
is : I(Type of Irrigation) = 0.993 – 0.9941= -0.0011.
 Sprinkler->Distance-> Height…>Left
P=4, N=4
= (4/(8))log 2 (4/(8))-(4/(8))log 2 (4/(8))
= 0.5log2(0.5))-0.5log2(0.5)
== -0.5*-1 - 0.5*-1 = 1.0
Type
I
 Drip->Distance->Left
P=9, N=7
= -(9/(16))log 2 (9/(16))-(7/(16))log 2 (7/(16))
= -0.562log2 (0.562))-0.437log2 (0.437)
= (-0.562*-0.831)-(0.437*-1.194) = 0.9888
 Drip->Distance-> Right
P=9, N=7
= (9/(16))log 2 (9/(16))-(7/(16))log 2 (7/(16))
= 0.562log2 (0.562))-0.437log2 (0.437)
= (-0.562*-0.831)-(0.437*-1.194) = 0.9888
Sub Type
P
N
Drip(D)
Sprinkler(S)
D->Distance(DD)
S->Distance(SD)
DD-Height(DDH)
SD-Height(SDH)
DDH-Qty(DDHQ)
DDH-Qty(SDHQ)
DDHQ->Plenty(DDHQP)
SDHQ->Limited(SDHQL)
35
18
17
17
09
09
09
08
04
05
29
14
15
15
07
07
07
08
04
03
I(PSubi,
NSubi)
0.993
0.9882
0.9996
0.9996
0.9888
0.9888
0.9888
1.0000
1.0000
0.9460
Table 1: Information Gain summary
V. CONCLUSION
Classification methods are typically strong in modeling
interactions. The goal of classification result integration
algorithms is to generate more certain, precise and accurate
system results. Numerous methods have been suggested for
the creation of ensemble of classifiers. The table lists the
information gains with various types of parameters that
could be used in the agriculture irrigation by a farmer in
his/her fields. By comparing the various parameters
considered, one could identify the best suitable method of
irrigation for his/her fields, which could help them in the
cultivation, and thus maximize the profit.
 Sprinkler->Distance-> Left
P=9, N=7
= -(9/(16))log 2 (9/(16))-(7/(16))log 2 (7/(16))
= -0.562log2 (0.562))-0.437log2(0.437)
= (-0.562*-0.831)-(0.437*-1.194) = 0.9888
 Sprinkler->Distance-> Right
P=8, N=8
= -(8/(16))log 2 (8/(16))-(8/(16))log 2 (8/(16))
= -0.5log2(0.5))-0.5log2(0.5)
= -0.5*-1 - 0.5*-1 = 1.0
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 Drip->Distance-> Height…>Left
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= -(4/(8))log 2 (4/(8))-(4/(8))log 2 (4/(8))
= -0.5log2(0.5))-0.5log2(0.5)
= -0.5*-1 - 0.5*-1 = 1.0
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 Drip->Distance-> Height…>Right
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International Journal of Emerging Technology and Advanced Engineering
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