用深度学习精准识别棉叶病害，准确率达98%
CottonLeafVision: An Explainable and Robust Deep Learning Framework for Cotton Leaf Disease Classification
- 基于DenseNet201模型，结合多种可解释技术提升可靠性
- 在真实田间条件下实现98%分类准确率，抗噪性强
- 适合农业监测与智能植保系统，支持实际部署
全球范围内，棉花是极具经济价值的作物，纺织业高度依赖它。因此，精准识别和检测棉叶病害对经济稳定至关重要。本文旨在开发一个高精度的棉叶病害分类与检测框架——CottonLeafVision。我们评估了DenseNet201、InceptionV3和VGG19等多个预训练深度卷积神经网络，在公开的棉叶病害图像数据集上进行测试。该数据集包含七类样本：六类病害和一类健康状态，图像采集自不同田间环境，反映真实世界挑战。其中，DenseNet201模型达到最高分类准确率98%。为增强模型的可靠性和可解释性，我们引入梯度加权类激活映射（Grad-CAM）、遮挡敏感性分析及对抗训练等方法，显著提升模型对噪声的鲁棒性。最后，我们构建了原型系统，实现在真实农业场景中的应用。本研究展示了深度学习模型在实际棉叶病害管理中的有效能力。
原文摘要 · Abstract (English)
Globally, cotton is a highly economically beneficial crop, as the textile industry heavily depends on it. So, the precise identification and detection of cotton leaf disease is crucial for economic stability. The development goal of "CottonLeafVision" is to accurately classify and detect cotton leaf disease. With this goal, we have evaluated multiple pretrained Deep Convolutional Neural Networks, including DenseNet201, InceptionV3, and VGG19 on a publicly available cotton leaf disease image dataset. This image dataset includes seven classes, six disease classes, and one healthy class, collected under various field conditions reflecting real-world challenges. Among these pretrained models, with DenseNet201, we have achieved the highest classification accuracy of 98%. To enhance the model reliability and interpretability, we have implemented different techniques and methods such as Gradient-weighted Class Activation Mapping (Grad-CAM), occlusion sensitivity analysis and adversarial training to increase the noise resistance of the model. Finally, we have developed a prototype in order to utilize the model's capabilities on real life agriculture. This paper shows the deep learning model's capabilities to classify the disease in real-life cotton disease management situations.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。