Data Mining Techniques in the Agricultural Sector

Abstract

Data mining denotes discovering the useful information from large volume of data. It has useful areas of implementation in many sectors. In this work, we are mainly concentrating on Data Mining Techniques in the Agricultural sector (DMTA). Agriculture is a fundamental human need. The economy is greatly affected by the Agricultural Sector in a Nation like India. Agricultural sector's success or failure depends on the weather conditions and soil parameters. Presently, farmers are growing crops based on their knowledge acquired from the past generation. Since the traditional technique of farming is practiced, plants are excessive or scarce without meeting the real necessity. No scheme is in place to educate the farmers, and there is a variety of new techniques available to solve such issues. This paper presents the results obtained by analyzing the trends followed in the past 10 years using DMTA Model to forecast optimal parameters required to get highest production for Ragi, Groundnut and Paddy. Techniques used for analysis are Bisecting K-Means, DBSCAN, OPTICS, Hierarchical Complete Linkage and STING. All the Districts of Karnataka and various parameters of individual crops are considered.