Rice Leaf Disease Classification Using Fine-Tuned ResNet152V2: Benchmarking CNN Models

This study presents a fine-tuned ResNet152V2-based transfer learning model for six-class rice leaf disease classification using a balanced dataset of 2,100 images. The proposed approach incorporates standardized preprocessing, stratified data splitting, and controlled training-time augmentation to improve generalization and reduce overfitting. A systematic comparison with VGG16 and MobileNetV2 demonstrates the accuracy–efficiency trade-off among the evaluated architectures. The proposed FT-ResNet152V2 achieves 96.24% test accuracy, with 0.96 precision, recall, and F1-score, outperforming VGG16 and MobileNetV2. Comprehensive evaluation using confusion matrices, ROC curves, and precision–recall curves further demonstrates its strong classification capability, while model complexity analysis highlights the computational cost associated with its higher accuracy.