Hybrid explainable DeiT-based framework for plant disease classification and severity estimation

Abstract

The detection of plant diseases is essential for preserving agricultural productivity and food security; however, existing approaches often suffer from limited interpretability and generalization capability. This study proposes a hybrid deep learning framework based on Data-efficient Image Transformers (DeiT) for plant disease classification and severity estimation. The framework employs DeiT-Base, DeiT-Small, and DeiT-Tiny models to capture global contextual dependencies in plant leaf images. To improve interpretability, a hybrid Explainable Artificial Intelligence (XAI) module is introduced by combining Gradient-weighted Class Activation Mapping (Grad-CAM) for local feature attribution with Attention Rollout for global dependency visualization. In addition, HSV-based segmentation is applied after classification to isolate disease-relevant regions for damage ratio computation, severity estimation, and explanation refinement. The damage ratio is further integrated with Hybrid XAI attention maps to estimate disease severity. Experiments were conducted on the New Plant Diseases Dataset (Augmented), comprising 70,295 training images and 17,572 validation images across 38 disease classes and 14 plant species. The proposed DeiT-Base model achieved a maximum classification accuracy of 99.13%, outperforming several CNN architectures, including ResNet50, DenseNet121, MobileNetV3, EfficientNet-B4, and InceptionV3. Furthermore, the proposed Hybrid XAI framework demonstrated superior interpretability performance in terms of Focus Score, Background Noise, Signal-to-Noise Ratio (SNR), and Entropy compared with individual explanation methods. Overall, the proposed framework improves classification accuracy, enhances model transparency, and provides meaningful disease severity estimation, making it a promising solution for intelligent precision agriculture.