A Machine Learning Technique for Rice Blast Disease Severity Prediction Using K-Means SMOTE Class Balancing

Abstract

Rice blast disease is strongly dependent on environmental and climate factors. This paper demonstrates the integration of a rice blast disease severity prediction model based on climate factors, providing a decision-support framework for farmers to overcome these problems. The major contribution of the proposed study is to predict the severity of rice blast disease using the linear SVM model. Prediction of rice blast disease severity is divided into four classes: 0, 1, 2, and 3. Data imbalance is the most challenging problem in multi-class classification. This study has efficiently handled imbalanced data using k-means SMOTE and SMOTE oversampling techniques to balance training and testing data. Finally, cross-location and cross-year models are developed using a linear support vector machine and predict the severity of rice blast disease to the classes 0, 1, 2, 3, respectively. Cross-year and cross-location models are cross-validated using five-fold cross-validation.