Crop yield prediction using data mining: an efficient data modeling approach

Motivation for this research comes from the agricultural point of view that there is a lot of work for the farmers to be done manually. So this research deals with the automatic classification approach which provides help to the farmers and decrease their efforts. With the change of climate in the world, the mainstreams of the crops in the agricultural field are severely affected in performance in previous decades. The crop prediction yielding should be healthy to help the farmers for taking suitable procedures for selling and loading/storage. Such estimations on the yielding of the crops will also assist the industries based on the farming and production of the crops for development of the logistics of their commercials. Numerous approaches of estimating and crop yielding have been established in the previous times with variable success rates. This paper deals with the prediction of the wheat crop yielding using data mining approaches. The paper deals with the clustering approach using k-means clustering and feature extraction using independent component analysis and the classification is done using linear discriminant analysis and the simulation is done using MATLAB environment. From the result and discussions it can be noticed that our proposed approach is able to achieve less error rate probabilities and high signal to noise ratio. The proposed approach is able to achieve mean square error rate of 0.5 and peak signal to noise ratio of 33.18 db.