发表机构
Leading University（莱丁大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对茶叶病害人工检测的缺陷，提出轻量级弱监督CNN模型LightTeaNet，采用深度可分离卷积、通道注意力及CAM技术，在无人工标注下实现多标签茶叶病害检测与定位，性能具竞争力且资源高效。
AI 中文摘要
茶叶是南亚和东南亚许多地区的重要作物，但多种病害仍会降低茶叶的产量和品质，阻碍茶叶生产。传统人工检测方法缺乏一致性、耗费大量人力，且依赖广泛的监测工作。本文提出一种名为LightTeaNet的轻量级卷积神经网络（CNN），用于茶叶的弱监督多标签分类与病害定位。与需要大量边界框标注的传统目标检测模型（如YOLO）不同，LightTeaNet直接从图像级标签中学习，并采用类别激活映射（CAM）自动定位病害区域。为提升参数效率，该网络集成了深度可分离卷积；为增强特征辨识度，还集成了通道注意力机制。实验结果显示，LightTeaNet在无任何人工标注的情况下，准确率达0.9615，召回率达0.8772，F1分数达0.9179，mAP@0.50为0.1810，其定位性能具有竞争力。这些结果验证了该模型是一种可解释且资源高效的框架，可用于农业中的智能病害监测。
英文摘要
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 mAP@0.50=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.
Comments24 pages, 11 figures, 5 tables
Journal refNeural Computing and Applications (2026)
DOI：10.1007/s00521-026-12329-z
