Interpretable citrus disease classification through GradCAM++ LIME and vision transformer-based deep learning

Deep learning models are frequently implemented as black boxes, which restricts their application in sensitive sectors such as agriculture, despite their ability to achieve high classification accuracies. To resolve this issue, we introduce a comprehensive interpretability framework that implements Grad-CAM, Grad-CAM++, and LIME techniques on the most effective CNN + ViT + SVM ensemble model for the detection of citrus diseases. The proposed model obtained classification accuracies of 99% and 97% by augmenting the real-world Lemon and Orange datasets with 1600 and 4000 images, respectively. Interpretability was implemented on the DenseNet121 (Orange) and InceptionV3 (Lemon) backbones. The model focused on specific textures related to diseases, which was shown by the detailed image analysis from LIME, while Grad-CAM and Grad-CAM ++ highlighted clear areas of lesions with high detail. A perfect score of 1.00 over all assessed courses was achieved through quantified assessment using Intersection over Union (IoU) and Dice Coefficient, and confirmed accuracy in visual explanation. In addition, intra-class compactness and inter-class separability were confirmed using feature-space t-SNE mappings. This visual explanation framework enhances transparency and builds user trust in machine learning-based disease management decision making in practice through clearly interpretable visual explanations for both multi-class and binary classification tasks. This research is a major advance towards developing trustworthy and comprehensible AI (XAI) solutions for agricultural diagnosis, through providing visual explanations to both multi-class and binary classification tasks. The approach is scalable and model-independent and is capable of integration into real-time and mobile decision support tools.