Early detection of plant diseases is essential for effective crop disease management to prevent
yield loss. In this study, we developed a methodology for classifying diseases in rice leaves using four
deep learning models and a dataset with 2658 images of healthy and diseased rice leaves. Four models,
namely LeafNet, Modified LeafNet, MobileNetV2, and Xception, were compared. The Modified LeafNet
model involved updates to LeafNet’s architectural parameters, whereas transfer learning techniques were
applied to the MobileNetV2 and Xception pretrained models. The optimal hyperparameters for training
were determined by considering several factors such as batch size, data augmentation, learning rate, and
optimizers. The Modified LeafNet model achieved the highest accuracies of 97.44% and 87.76% for the
validation and testing datasets, respectively. In comparison, LeafNet obtained 88.92% and 71.84%, Xception
obtained 88.64% and 71.95%, and MobileNetV2 obtained 82.10% and 67.68% for the validation and test
accuracies on the same datasets, respectively. This study contributes to the development of automated disease
classification systems for rice leaves, thereby leading to increased agricultural productivity and sustainability.