Plant Disease Classification Using ResNet50 with Saliency Map and grad-cam-BasedExplainability

Abstract

This study presents a comprehensive deep learning-based framework for multiclass plant disease classification using high-resolution leaf images, with a particular focus on evaluating the performance of convolutional neural networks (CNNs) and a hybrid ResNet + Vision Transformer (ViT) architecture. A curated dataset comprising 15,200 training and 3,800 validation images spanning 38 classes across multiple crops, including tomato, apple, grape, corn, potato, strawberry, peach, pepper, orange, blueberry, raspberry, soybean, and squash, was subjected to preprocessing steps such as resizing, normalization, and data augmentation to enhance model robustness. Multiple CNN architectures— including ResNet-50, MobileNetV2, and EfficientNet-B0—were trained and compared against the hybrid ResNet + ViT model. All models were fine-tuned using the AdamW optimizer and cross-entropy loss, with early stopping applied to prevent overfitting and ensure generalization. Additionally, interpretability techniques including Grad-CAM and Saliency Maps were employed to visualize disease-relevant regions, while segmentation-based analysis was performed to localize affected areas on leaves. Among all architectures evaluated, ResNet-50 achieved the highest validation accuracy of 98.74%, while the hybrid ResNet + ViT model recorded a competitive accuracy of 98.58%, demonstrating the effectiveness of hybrid architectures in capturing both local and global features. The experimental results highlight the potential of transformer-based and lightweight CNN models to deliver highly accurate, interpretable, and computationally efficient solutions for automated multi-class plant disease detection, offering valuable support for precision agriculture and crop management practices.