Explainable Tomato and Pepper Leaf Disease Detection Using YOLOv12 and Grad-CAM

Abstract

Deep learning–based object detection models have shown strong potential for automated plant disease detection from leaf images. Among these models, YOLO architectures are widely used due to their ability to achieve high detection accuracy while maintaining real-time performance. However, despite these advantages, the adoption of such systems in agricultural practice remains limited. One of the main reasons is that their predictions are often difficult to interpret, which can reduce the confidence of farmers and agricultural experts, who need to understand the basis of model predictions before relying on them for decision-making. To address this issue, this paper proposes an explainable deep learning framework that combines a YOLOv12-based detection model with explainable artificial intelligence techniques. The proposed approach is evaluated on a self-generated dataset of plant leaf images. The experimental results show that the proposed YOLO model achieves satisfactory detection performance, confirming its suitability for plant disease detection tasks. In addition to performance evaluation and to improve transparency, Gradient-weighted Class Activation Mapping is employed to generate visual explanations of the model’s predictions. The resulting heatmaps reveal that the network consistently concentrates on relevant diseased regions of the leaves, indicating that the detection decisions are guided by meaningful visual features. By combining accurate detection performance with visual explanations, the proposed framework aims to provide a more transparent and trustworthy solution for deep learning–based plant disease detection.