Main Article Content
Abstract
Crop prediction is an important agricultural problem. To address this problem, clustering and classification techniques are used for crop yield prediction. It is the one of the most commonly used intelligent technique based on data analytics concepts to predict the crop yield for maximizing the crop productivity. Machine learning techniques can be used to improve prediction of crop yield under different climatic scenarios. The Baysian network Classification is a supervised learning model which means temperature and rainfall analyzes the crop data used for classification and probability values of Rice, Coconunt, Arecanut, Black pepper and Dry ginger crops. .The Baysian network Classification analysis technique is used for exploring the dataset.For the present study the mean absolute error (MAE), root mean squared error (RMSE), relative absolute error (RAE) and root relative squared error (RRSE) were calculated. The experimental results showed that the performance of other techniques on the same dataset was much better compared to SMO.