Abstract—Over the last few decades, Information Technology become a part of our daily life. Technology breakthroughs have made in industry and services as well as in agriculture. The connection between Information Technology and Agriculture has become an interesting area of research in yield prediction subject to the available data. A farmer harvest not only crops but also growing amount of data. A farmer wants to know about the applications of recent technologies in agriculture. Such technological requirement from the farmer lead to extracting the knowledge from the available data. The knowledge extraction methods in data mining are to be explored in order to obtain the crop yield prediction. Lots of data mining techniques were used in agriculture. Some of the widely used data mining techniques over agriculture data sets are Multiple Linear Regression, Density based Clustering Technique, K-Means approach, K-Nearest Neighbor, Artificial Neural Networks, Support Vector Machines. In this context the main aim of the paper is to model and to optimize the available by means of data mining techniques to predict the crop yield. This paper presents a brief idea of the widely used data mining techniques over agriculture data sets and deal with Density based Clustering Technique.

Index Terms—information technology, agriculture, yield prediction, data mining, multiple linear regression, density based clustering technique, K-Means approach, K-Nearest neighbor, artificial neural networks, support vector machines

Cite: B Vishnu Vardhan, D. Ramesh, and O Subhash Chander Goud, "Density Based Clustering Technique on Crop Yield Prediction," International Journal of Electronics and Electrical Engineering, Vol. 2, No. 1, pp. 56-59, March 2014. doi: 10.12720/ijeee.2.1.56-59