Abstract
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.
Abstract (translated)
南瓜叶病害是影响农业生产力的重要威胁，及时且精准的诊断对于有效管理这些疾病至关重要。传统的识别方法既费力又容易出错，因此迫切需要自动化解决方案。本研究使用了“南瓜叶病害数据集”，该数据集包含2000张高分辨率图像，并分为五个类别：霜霉病、白粉病、花叶病、细菌斑点病和健康叶片。数据集从多个农业现场严格收集而成，确保模型训练具有强大的代表性。 我们探索了多种高效深度学习架构，包括DenseNet201、DenseNet121、DenseNet169、Xception、ResNet50、ResNet101和InceptionResNetV2，并观察到ResNet50表现最为出色，其准确率为90.5%，且精度、召回率和F1分数相当高。我们还采用了可解释的人工智能（Explainable AI, XAI）方法，如Grad-CAM、Grad-CAM++、Score-CAM和Layer-CAM，以提供模型决策过程的有意义表示，从而增强了理解和信任。 这些发现表明ResNet50具有革新南瓜叶病害检测的巨大潜力，能够实现更早且更为准确的治疗。
URL
https://arxiv.org/abs/2501.05449
