Comparison of LBP, GLCM, and Canny Feature Extraction Methods in Rice Leaf Disease Classification Using KNN

Abstract

Accurate and timely identification of rice leaf diseases plays a crucial role in supporting early disease control efforts in agriculture. This study aims to compare the performance of three image feature extraction methods—Local Binary Pattern (LBP), Gray Level Co-occurrence Matrix (GLCM), and Canny Edge Detection—in classifying three types of rice leaf diseases: Bacterial leaf blight, Brown spot, and Leaf smut. Each method was evaluated based on its confusion matrix as well as key performance metrics, including accuracy, precision, recall, and F1-score. Experimental results show that LBP achieved the highest classification performance with an accuracy of 92.06%, followed by GLCM at 78.57% and Canny at 66.67%. In addition to accuracy, LBP also outperformed the other methods across all evaluation metrics. These findings indicate that the local texture features captured by LBP are more effective in distinguishing disease types compared to the global texture features from GLCM and edge-based features from Canny. Therefore, LBP is recommended as a superior feature extraction method for automated classification systems of rice leaf diseases based on digital imagery.