轻量级网络实现茶叶病害多标签识别与定位，无需标注框。
LightTeaNet: A Weakly Supervised Lightweight CNN for Multi-Label Tea Leaf Disease Detection and Localization
- 基于图像标签训练，用注意力机制提升特征区分力。
- 准确率96.15%，召回率87.72%，定位mAP达0.1810。
- 适合资源受限场景，模型小且可解释性强。
茶叶是南亚和东南亚重要作物，但多种病害影响产量与品质。传统人工检测方式效率低、不一致且依赖大量人力。本文提出轻量级卷积神经网络LightTeaNet，用于弱监督下的多标签茶叶病害分类与定位。该模型直接从图像级标签学习，利用类激活图（CAM）自动定位病害区域，无需繁琐的边界框标注。为提升参数效率，引入深度可分离卷积；为增强特征判别能力，集成通道注意力模块。实验显示，LightTeaNet在无任何手动标注条件下，取得精度0.9615、召回率0.8772、F1分数0.9179，且在[email protected]上达到0.1810，表现优异。结果验证了其在农业智能监测中兼具可解释性与资源高效性。
原文摘要 · Abstract (English)
Tea is known as an important crop in many parts of South and Southeast Asia, yet the production of tea is still hampered by the multiple diseases that decrease the quantity and quality. Traditional methods of inspection, which are manual, are not consistent, labor-intensive, and depend on extensive monitoring. This paper introduces a lightweight convolutional neural network (CNN) designed for weakly supervised multi-label classification and disease localization in tea leaves called LightTeaNet. LightTeaNet learns directly from image-level labels and employs Class Activation Mapping (CAM) to localize disease-affected regions automatically, unlike conventional object detection models such as YOLO, which require extensive bounding box annotations. For Parameter efficiency, the network integrates Depthwise Separable Convolutions, and for enhanced feature discrimination, it integrates Channel Attention. LightTeaNet has achieved a Precision of 0.9615, a Recall of 0.8772, and an F1-score of 0.9179, while it shows [email protected]=0.1810 without any manual annotations, which delivers a competitive localization performance in the experimental results. These results validate the model as an interpretable as well as a resource-efficient framework for intelligent disease monitoring in agriculture.
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