Statistical Based Agricultural Data analysis

This Paper is Basically applied to the Advancement in Farming by technological Evolution as the growth in Computing and information Assessment, Retrieval and Storage have provided vast amount of Data. Data mining Techniques have been extensively seed n large amount of Datasets and Variables. But the main challenge is to extract information from this data which results n various methodologies and techniques such as Data Mining that can easily provide Results and Conclusions. 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 HM, K-Nearest Neighbor(KNN), Decision Tree(DT) and Support Vector Machines(SVM) in Agricultural Data as a tool for mining. Different Data sets are evaluated and hence outcomes with Different Data Mining Techniques. This paper discusses a process model for analyzing data, and describes the support that provides for this model.