Interpretable Deep Learning Framework for Early Classification of Tomato and Grapevine Leaf Diseases

The integration of artificial intelligence (AI) in precision agriculture has facilitated significant advancements in crop health monitoring, particularly in the early identification and classification of foliar diseases. Accurate and timely diagnosis of plant diseases is critical for minimizing crop loss and enhancing agricultural sustainability. In this study, an interpretable deep learning model—referred to as the Multi-Crop Leaf Disease (MCLD) framework—was developed based on a Convolutional Neural Network (CNN) architecture, tailored for the classification of tomato and grapevine leaf diseases. The model architecture was derived from the Visual Geometry Group Network (VGGNet), optimized to improve computational efficiency while maintaining classification accuracy. Leaf image datasets comprising healthy and diseased samples were employed to train and evaluate the model. Performance was assessed using multiple statistical metrics, including classification accuracy, sensitivity, specificity, precision, recall, and F1-score. The proposed MCLD framework achieved a detection accuracy of 98.40% for grapevine leaf diseases and a classification accuracy of 95.71% for tomato leaf conditions. Despite these promising results, further research is required to address limitations such as generalizability across variable environmental conditions and the integration of field-acquired images. The implementation of such interpretable AI-based systems is expected to substantially enhance precision agriculture by supporting rapid and accurate disease management strategies.