<![CDATA[Rice plant diseases can reduce agricultural productivity and yield quality, highlighting the need for rapid and accurate identification methods to support precision agriculture. This study evaluates four deep learning architectures—ResNet-18, VGG-16, MobileNetV2, and Inception V3—for rice leaf disease classification using digital images. The dataset underwent preprocessing and augmentation before training, while transfer learning with ImageNet pre-trained weights was applied. Models were trained using CrossEntropyLoss and Adam optimizer with a learning rate of 0.0001 for 10 epochs. Performance was evaluated using accuracy, precision, recall, F1-score, AUC, and confusion matrix. Results demonstrate that the proposed ResNet-18 achieved the best overall performance, obtaining 96.94% accuracy, 100% precision, 95.45% recall, 96.18% F1-score, and 1.00 AUC. Inception V3 and MobileNetV2 showed competitive performance, whereas VGG-16 achieved relatively lower recall. These findings indicate that ResNet-18 provides effective feature representation and stable learning, demonstrating strong potential for rice leaf disease classification and future integration into mobile, field-camera, and IoT-based precision agriculture systems.]]>
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