基于深度学习与颜色特征规则的茶树炭疽病病斑量化分析

黄洁琼; 邓卓然; 任恒泽; 吕务云; 陆梦倩; 王新超; 陈雅楠; 王玉春

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茶叶科学 ›› 2026, Vol. 46 ›› Issue (3) : 475-488.

研究报告

基于深度学习与颜色特征规则的茶树炭疽病病斑量化分析

黄洁琼¹, 邓卓然¹, 任恒泽¹, 吕务云¹, 陆梦倩¹, 王新超², 陈雅楠^1,*, 王玉春^1,*

作者信息 +

Quantitative Analysis of Tea Anthracnose Lesions Based on Deep Learning and Color-Feature Rules

HUANG Jieqiong¹, DENG Zhuoran¹, REN Hengze¹, LÜ Wuyun¹, LU Mengqian¹, WANG Xinchao², CHEN Yanan^1,*, WANG Yuchun^1,*

Author information +

文章历史 +

摘要

炭疽病是危害茶树生长发育和茶叶品质的主要叶部病害之一。目前国内尚未建立茶树对该病害的抗性鉴定标准,常规鉴定评价工作主要借鉴其他作物,且相关评价方法依赖人工目测,造成评价方法主观性强、评价工作效率较低和评价结果准确性较弱等问题,严重制约了茶树良种选育工作进度和规范性。基于深度学习和图像识别技术,首先利用掩膜区域卷积神经网络（Mask R-CNN）实现田间病叶的实例分割与定位;随后在叶片感兴趣区域（ROI）内融合HSV颜色空间阈值规则与绿色度指数（Excess green,ExG）进行病斑精细分割,并根据病斑面积占比构建0~9级分级标准,开发了自动化评价系统。结果显示,模型在叶片实例检测上取得叶片掩膜分割交并比（mIoU）为92.56%,IoU阈值为0.5时的平均精度（R_AP@0.5）为98.91%;在病斑区域检测一致性验证中,自动化测量的病斑面积比例与Fiji-Weka工具生成的人工标注值极显著相关（Pearson’s r=0.946,P<0.001）,达到了可替代人工主观评价病害分级标准的精度要求。

Abstract

Anthracnose is one of the major foliar diseases that threatens the growth and development of tea plants and compromises tea quality. At present, no standardized criterion has been established in China for evaluating tea resistance to this disease. Conventional identification and evaluation practices are largely adapted from other crops, and the associated methods rely on visual inspection, resulting in strong subjectivity, low efficiency and limited accuracy. These shortcomings severely constrain both the progress and standardization of elite tea cultivar breeding. In this study, we developed an automated evaluation framework based on deep learning and image analysis. First, Mask R-CNN was used to localize and perform instance segmentation of diseased leaves from field images. Subsequently, within the leaf region of interest (ROI), fine lesion segmentation was achieved by integrating HSV color-space thresholding rules with the Excess Green (ExG) index. Based on the lesion area ratio, a 0-9 severity grading standard was established, and an automated evaluation system was developed. The results show that the model achieved mean of intersection over union (mIoU) of 92.56% for leaf mask segmentation and ratio of average precision at IoU=0.5 (R_AP@0.5) of 98.91% for leaf instance detection. For consistency validation of lesion detection, the lesion area ratios measured by the automated method were highly significantly correlated with manual reference values generated using the Fiji-Weka tool (Pearson’s r=0.946, P<0.001), meeting the accuracy requirement to replace subjective manual grading.

导出引用

黄洁琼, 邓卓然, 任恒泽, 吕务云, 陆梦倩, 王新超, 陈雅楠, 王玉春. 基于深度学习与颜色特征规则的茶树炭疽病病斑量化分析[J]. 茶叶科学. 2026, 46(3): 475-488

HUANG Jieqiong, DENG Zhuoran, REN Hengze, LÜ Wuyun, LU Mengqian, WANG Xinchao, CHEN Yanan, WANG Yuchun. Quantitative Analysis of Tea Anthracnose Lesions Based on Deep Learning and Color-Feature Rules[J]. Journal of Tea Science. 2026, 46(3): 475-488

中图分类号： S571.1 S435.711

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