Prognosis for Crop Yield Production by Data Mining Techniques in Agriculture

Prognosis for Crop Yield Production by Data Mining Techniques in Agriculture

Divya Singh (Amity University, India) and Dinesh Sharma (Amity University, India)
DOI: 10.4018/978-1-5225-8027-0.ch006

Abstract

In agriculture, data mining technique is used for extracting information from a large dataset. The techniques for data mining are used in yield prediction for crop at broader spectrum. Agricultural system is very complex and vast therefore to deal with large data situation is a great factor. Different consultancy, industrial production department, organization related to crops is taking keen interest towards crop yield prediction. Here the focus is on the applicability of data mining techniques in agricultural field. The classification and clustering techniques of data mining are used recently in agriculture field. Data mining technology merged with the rapid development of computer science. This chapter focuses on collecting information and overcome the short comes of manual data handling and prediction of yield results of crop production. Data mining is a prominent agricultural research area for analysis of crop yield. These predictions are a very important in solving agricultural problems for crops.
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Data Mining Techniques/Method

Data Mining incorporates many techniques like clustering, classification, machine learning, Support Vector Machines, Regression, Association Rules etc. Further, these techniques can be applied to the dataset by different algorithms. Here we focused on two broad classifications i.e. clustering, classification technique.

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