Data Mining Techniques and Applications to Agricultural Yield Data

Abstract: Data Mining is emerging research field in Agriculture crop yield analysis. In this paper our focus is on the applications of Data Mining techniques in agricultural field. Different Data Mining techniques are in use, such as K-Means, K-Nearest Neighbor(KNN), Artificial Neural Networks(ANN) and Support Vector Machines(SVM) for very recent applications of Data Mining techniques in agriculture field. In this paper consider the problem of predicting yield production. Yield prediction is a very important agricultural problem that remains to be solved based on the available data. The problem of yield prediction can be solved by employing Data Mining techniques. This work aims at finding suitable data models that achieve a high accuracy and a high generality in terms of yield prediction capabilities. For this purpose, different types of Data Mining techniques were evaluated on different data sets.

Keywords: Data Mining, K-Means, K-Nearest Neighbor, Artificial Neural Networks, Support Vector Machines, Yield Prediction.