Pumpkin leaf diseases are significant threats to agricultural productivity, requiring a timely and precise diagnosis for effective management. Traditional identification methods are laborious and susceptible to human error, emphasizing the necessity for automated solutions. This study employs on the "Pumpkin Leaf Disease Dataset", that comprises of 2000 high-resolution images separated into five categories. Downy mildew, powdery mildew, mosaic disease, bacterial leaf spot, and healthy leaves. The dataset was rigorously assembled from several agricultural fields to ensure a strong representation for model training. We explored many proficient deep learning architectures, including DenseNet201, DenseNet121, DenseNet169, Xception, ResNet50, ResNet101 and InceptionResNetV2, and observed that ResNet50 performed most effectively, with an accuracy of 90.5% and comparable precision, recall, and F1-Score. We used Explainable AI (XAI) approaches like Grad-CAM, Grad-CAM++, Score-CAM, and Layer-CAM to provide meaningful representations of model decision-making processes, which improved understanding and trust in automated disease diagnostics. These findings demonstrate ResNet50's potential to revolutionize pumpkin leaf disease detection, allowing for earlier and more accurate treatments.

本研究针对传统南瓜叶病诊断方法效率低下、易出错的问题，提出了一种自动化的深度学习解决方案。通过分析多种深度学习架构，发现ResNet50在南瓜叶病检测中表现最佳，准确率达到90.5%，并结合可解释的人工智能方法提升了模型的透明度和可靠性。研究结果显示，ResNet50在南瓜叶病检测中的应用潜力巨大，有望实现更早和更精确的病害管理。

可解释的人工智能增强深度学习用于南瓜叶病检测：CNN架构的比较分析