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Reseach Article

A Brief survey of Data Mining Techniques Applied to Agricultural Data

by Hetal Patel, Dharmendra Patel
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 95 - Number 9
Year of Publication: 2014
Authors: Hetal Patel, Dharmendra Patel

Hetal Patel, Dharmendra Patel . A Brief survey of Data Mining Techniques Applied to Agricultural Data. International Journal of Computer Applications. 95, 9 ( June 2014), 6-8. DOI=10.5120/16620-6472

@article{ 10.5120/16620-6472,
author = { Hetal Patel, Dharmendra Patel },
title = { A Brief survey of Data Mining Techniques Applied to Agricultural Data },
journal = { International Journal of Computer Applications },
issue_date = { June 2014 },
volume = { 95 },
number = { 9 },
month = { June },
year = { 2014 },
issn = { 0975-8887 },
pages = { 6-8 },
numpages = {9},
url = { },
doi = { 10.5120/16620-6472 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T22:18:58.961547+05:30
%A Hetal Patel
%A Dharmendra Patel
%T A Brief survey of Data Mining Techniques Applied to Agricultural Data
%J International Journal of Computer Applications
%@ 0975-8887
%V 95
%N 9
%P 6-8
%D 2014
%I Foundation of Computer Science (FCS), NY, USA

As with many other sectors the amount of agriculture data based are increasing on a daily basis. However, the application of data mining methods and techniques to discover new insights or knowledge is a relatively a novel research area. In this paper we provide a brief review of a variety of Data Mining techniques that have been applied to model data from or about the agricultural domain. The Data Mining techniques applied on Agricultural data include k-means, bi clustering, k nearest neighbor, Neural Networks (NN) Support Vector Machine (SVM), Naive Bayes Classifier and Fuzzy c-means. As can be seen the appropriateness of data mining techniques is to a certain extent determined by the different types of agricultural data or the problems being addressed. This survey summarize the application of data mining techniques and predictive modeling application in the agriculture field.

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Index Terms

Computer Science
Information Sciences


Agriculture Data Mining k-means bi clustering k nearest neighbor Artificial Neural Network (ANN) Support Vector Machine Naive Bayesian Classifier Fuzzy c-means