<![CDATA[Rice productivity is significantly affected by leaf diseases that reduce crop yield and quality. Conventional disease identification methods rely on manual observation, which is often time-consuming, subjective, and prone to misclassification due to similarities in visual symptoms. This study proposes an automated image-based classification system to detect rice leaf diseases accurately and efficiently. The system utilizes a deep learning model based on convolutional neural networks to classify rice leaf images into three disease categories: neck blast, leaf blight, and rice hispa. A dataset consisting of 3,631 images was used, with 80% allocated for training, 10% for validation, and 10% for testing. Image preprocessing techniques, including resizing, normalization, and augmentation, were applied to improve model performance and generalization. The experimental results show that the proposed model achieved a testing accuracy of 97.80%, with high precision, recall, and F1-score across all classes. The trained model was then deployed into a web-based system that enables users to upload images and obtain real-time classification results. The findings demonstrate that the proposed system provides a reliable and practical solution for early disease detection, supporting precision agriculture and improving decision-making for farmers. The system also offers potential for further development into mobile and integrated smart farming platforms.]]>
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