Advanced Machine Learning Techniques for Disease Management in Paddy Crop for Sustainable Rice Production

Abstract

Rice blast disease is strongly dependent on environmental and climate factors. Integration of rice blast disease severity prediction model based on climate factors provides decision support framework for farmers to overcome from the problems of climate change scenarios. Major contribution of the proposed study is to predict the severity of rice blast disease using Linear SVM model. Prediction of severity of rice blast disease is Severity of rice blast disease is divided into four classes 0,1,2 and 3. Data imbalance is the most difficult problem in multi-class classification. Proposed study has handled imbalanced data efficiently using k-means SMOTE and SMOTE over sampling techniques to make training and testing data balance. Finally cross location and cross year models are developed using linear support vector machine and predicted severity of rice blast disease to the classes 0,1,2,3 respectively. Cross year and cross location models are cross validated using 5-fold cross validation.