Classification of Rice Leaf Diseases Using Support Vector Machine with HSV and GLCM-Based Feature Extraction

Abstract

This study aims to classify rice leaf diseases using the Support Vector Machine (SVM) algorithm based on image processing and feature extraction. A total of 600 rice leaf images were collected, each representing one of five disease types: bacterial blight, leaf smut, leaf blast, brown spot, and hispa. The images underwent preprocessing, including resizing, background removal, and feature extraction using HSV and GLCM methods. Extracted features were then used to train and test an SVM classification model. The evaluation using confusion matrix showed an overall accuracy of 83%, with class-specific F1-scores ranging from 0.72 to 0.90. These results indicate that SVM is effective in classifying rice leaf diseases and can potentially assist farmers in early disease detection to reduce crop loss.This study aims to classify rice leaf diseases using the Support Vector Machine (SVM) algorithm based on image processing and feature extraction. A total of 600 rice leaf images were collected, each representing one of five disease types: bacterial blight, leaf smut, leaf blast, brown spot, and hispa. The images underwent preprocessing, including resizing, background removal, and feature extraction using HSV and GLCM methods. Extracted features were then used to train and test an SVM classification model. The evaluation using confusion matrix showed an overall accuracy of 83%, with class-specific F1-scores ranging from 0.72 to 0.90. These results indicate that SVM is effective in classifying rice leaf diseases and can potentially assist farmers in early disease detection to reduce crop loss.