Web-Based Rice Leaf Disease Classification Using CNN
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.