An Efficient Explainable AI Method Combining CNN and SVM for Corn Leaf Disease Detection and Visualization

Agriculture plays a crucial role in Bangladesh's economy, with corn serving as a key crop. Plant diseases pose significant threats to agricultural productivity and economic stability worldwide. Effective monitoring and prediction are essential to mitigate these risks, as the prevalence of infectious diseases severely disrupts agricultural production. This study focuses on diagnosing agricultural diseases and pests affecting corn stalks using convolutional neural networks (CNN) combined with an SVM classifier. The proposed model achieves a classification accuracy of 98.34%, comparable to transfer learning models such as VGG-16 (97.42%) and VGG-19 (97.13%), while utilizing approximately 0.95 million parameters—about 145 times and 151 times fewer than VGG-16 and VGG-19, respectively. The model exhibits outstanding performance, with accuracy, recall, and F1 scores nearing 97% and an exceptional Area Under Curve (AUC) score of 99%. Furthermore, the model's compact design requires minimal disk space (approximately 4 MB) and features a significantly reduced parameter count. To enhance interpretability, the study integrates explainable AI techniques, including Gradient Weighted Class Activation Mapping (Grad-CAM), Shapley Additive Explanations (SHAP), and Local Interpretable Model-Agnostic Explanations (LIME). These methods provide visual explanations through heatmaps, highlighting regions critical for classification, thereby facilitating a clearer understanding of the disease diagnosis process.