Crop and Yield Prediction Model

Abstract

An agricultural sector necessitate for well defined and systematic approach for predicting the crops with its yield and supporting farmers to take correct decisions to enhance quality of farming. The complexity of predicting the best crops is high duet unavailability of crop knowledge-base. Crop prediction is an efficient approach for better quality farming and increase revenue. Use of data clustering algorithm is an efficient approach in field of data mining to extract useful information and give prediction. Various approaches have been implemented so far are worked either for crop prediction. Crop prediction model aiding farmers to take correct decision. This indeed helps in improving quality of farming and generate better revenue for farmers. Traditional clustering algorithms such as k-Means, improved rough k-Means and-means++ makes the tasks complicated due to random selection of initial cluster center and decision of number of clusters. Modified K-Means algorithm is thereby used to improve the accuracy of a system as it
achieves the high quality clusters duet initial cluster centric selection.