Wheat Yield Assessment using Decision Tree Algorithms

Prediction of agriculture yield with the help of data mining tools and techniques is one of the emerging research domains from past couple of years. This paper focuses on studying the effects of rainfall and relative humidity on three different stages of wheat crop and thus generating rules about crop yield using decision tree induction technique. A comparison of different classification algorithms is also done to discover the best rules about wheat crop yield with maximum accuracy and least errors. These study findings will help the farmers in future to predict their wheat crop yield in advance before crop growing season.