A Survey on Data Mining Approaches to Handle Agricultural Data

Agriculture is the backbone of our country, where every activities and events in the agriculture depends on the area or locality. This variation creates huge number of data’s, and that to be maintained effectively. These uncertain and dynamic data’s are very tedious to maintain and to manipulate. To overcome the above issues, several studies introduced numerous techniques in data mining. This paper gives a survey about the data mining techniques and tools used in agriculture. The data mining techniques used in agriculture which includes clustering techniques such as K-Means, Fuzzy, KNN, and classification techniques such as Bayesian, Artificial Neural network, SVM and Decision Tree etc. This also makes discussion about the problems of those techniques in the real time analysis.