用深度学习精准识别茶叶病害，准确率达99%。
TeaLeafVision: An Explainable and Robust Deep Learning Framework for Tea Leaf Disease Classification
- 基于DenseNet201模型与Grad CAM等技术提升可解释性
- 在真实场景数据集上达到99%测试准确率
- 适合农业实际应用，增强模型抗噪能力
作为仅次于水的世界第二大消费饮品，茶不仅是一种文化象征，更是具有深远影响的全球经济力量。精确识别和检测茶叶病害至关重要。为此，我们评估了多种卷积神经网络（CNN）模型，在teaLeafBD数据集上表现突出的包括DenseNet201、MobileNetV2和InceptionV3。该数据集包含七个类别：六种病害类和一个健康类，采集于多种田间条件，反映真实世界挑战。其中，DenseNet201在测试中取得99%的最高准确率。为提升模型可靠性与可解释性，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，增强模型抗干扰能力。最终开发原型系统，实现模型在真实农业场景中的落地应用。本研究展示了深度学习在实际茶叶病害检测与管理中的强大能力。
原文摘要 · Abstract (English)
As the worlds second most consumed beverage after water, tea is not just a cultural staple but a global economic force of profound scale and influence. More than a mere drink, it represents a quiet negotiation between nature, culture, and the human desire for a moment of reflection. So, the precise identification and detection of tea leaf disease is crucial. With this goal, we have evaluated several Convolutional Neural Networks (CNN) models, among them three shows noticeable performance including DenseNet201, MobileNetV2, InceptionV3 on the teaLeafBD dataset. teaLeafBD dataset contains seven classes, six disease classes and one healthy class, collected under various field conditions reflecting real world challenges. Among the CNN models, DenseNet201 has achieved the highest test accuracy of 99%. In order to enhance the model reliability and interpretability, we have implemented Gradient weighted Class Activation Mapping (Grad CAM), occlusion sensitivity analysis and adversarial training techniques to increase the noise resistance of the model. Finally, we have developed a prototype in order to leverage the models capabilities on real life agriculture. This paper illustrates the deep learning models capabilities to classify the disease in real life tea leaf disease detection and management.
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