A Study on Data Mining Approaches for Agricultural Intelligence

Abstract: Agricultural intelligence is a specific and emerging field of intelligence dedicated to an enhanced understanding of cultivation, productivity of crop, and minimized risk associated agriculture. Crop prediction is an important agricultural problem. To address this problem, crop prediction technique is used. It is the one of the most commonly used intelligent technique based on Data Mining (DM) concepts to predict the crop yield for maximizing the crop productivity. This paper studies and records the various data mining techniques available in the literature for better crop productivity.

Keywords: Data mining, Crop prediction, k-means, k-nearest neibour, Fuzzy sets, Regression, Classification, Neural network Association Rule.