<![CDATA[This study optimized the EfficientNet deep learning model based on digital images for rice disease detection, focusing on two main classes: Brown Spot and Leaf Scald, which are constraints to farmer productivity in Pagar Alam City. The dataset consisted of 780 images (476 Brown Spot, 304 Leaf Scald) processed through 224×224 resizing, normalization, data cleaning, and augmentation (rotation, flip, shear, shift, zoom) to improve generalization and reduce overfitting. The model was initialized with transfer learning from ImageNet, trained and fine-tuned at the final layer, and then evaluated using accuracy, precision, recall, and F1-score metrics. EfficientNet B0 showed a high training accuracy of up to 95% with a validation accuracy of around 80%, indicating good detection performance although there are still symptoms of overfitting that need further optimization. The model was then integrated into a web-based expert system for automatic diagnosis from leaf images and presentation of knowledge-based treatment recommendations, thereby accelerating early identification and supporting decision-making in the field. These results confirm EfficientNet's potential as the foundation for a practical, accurate, and applicable rice disease diagnosis system for local agriculture.]]>
Copyrights © 2025
