<![CDATA[Rice (Oryza sativa L.) is a major food crop that is susceptible to disease attacks, which can reduce farmers' productivity and yields. This study aims to develop a digital image-based rice leaf disease classification system using the Support Vector Machine (SVM) and Random Forest algorithms. The dataset consists of three disease classes (Blast, Blight, and Tungro), which are processed through pre-processing stages such as resizing, normalization, and augmentation. Feature extraction is performed using HSV histograms, RGB average values, and Gray Level Co-occurrence Matrix (GLCM) to obtain color and texture characteristics. The data is then divided with a ratio of 80:20 for model training and testing. The evaluation results show that Random Forest provides the best performance with an accuracy of 97.73%, precision and recall values ??above 0.94, and an average F1 score of 0.98. This study shows that a machine learning-based image classification approach can be an effective solution for early detection of diseases in rice plants.]]>
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