基于感兴趣区域图像分割的红茶茶毫品质数字化评价研究

茶叶科学 ›› 2025, Vol. 45 ›› Issue (4): 655-670.

基于感兴趣区域图像分割的红茶茶毫品质数字化评价研究

仝晨¹, 梁秀华², 王周立¹, 范冬梅¹, 林招水³, 吴小妹⁴, 阙杨战⁴, 金敏丽^1,5,*, 林杰^1,*

1.浙江农林大学茶学与茶文化学院,浙江临安 311300;
2.绍兴市经济作物技术推广中心,浙江绍兴 312000;
3.浙江龙额火山茶业有限公司,浙江台州 317600;
4.丽水农林技师学院,浙江丽水 323400;
5.浙江农林大学风景园林与建设学院,浙江临安 311300

收稿日期:2024-12-03 修回日期:2025-02-27 出版日期:2025-08-15 发布日期:2025-08-15
通讯作者: *278805795@qq.com;linjie@zafu.edu.cn
作者简介:仝晨,女,研究生,主要从事茶叶加工工程与品质数字化评价方面研究。
基金资助:
浙江省农业重大技术协同推广计划（2022XTTGCY04）、浙江农林大学科研发展基金项目（2023FR033）

Digital Evaluation of Black Tea Hairs Quality Based on Region of Interest Image Segmentation

TONG Chen¹, LIANG Xiuhua², WANG Zhouli¹, FAN Dongmei¹, LIN Zhaoshui³, WU Xiaomei⁴, QUE Yangzhan⁴, JIN Minli^1,5,*, LIN Jie^1,*

1. College of Tea Science and Tea Culture, Zhejiang A & F University, Lin'an 311300, China;
2. Shaoxing Economic Crop Technology Extension Center, Shaoxing 312000, China;
3. Zhejiang Long'e Volcano Tea Co., Ltd., Taizhou 317600, China;
4. Lishui Technician College of Agriculture and Forestry, Lishui 323400, China;
5. College of Landseape Architecture, Zhejiang A & F University,Lin'an 311300, China;

Received:2024-12-03 Revised:2025-02-27 Online:2025-08-15 Published:2025-08-15

摘要/Abstract

摘要： 茶毫是红茶外形品质的重要评价指标,当前主要依赖于专业人员的感官评价,主观性强且评语抽象,缺乏客观化、数字化的品质评价手段。为构建茶毫品质数字化评价方法,采集3个不同茶毫品质等级的祁门红茶样品图像,采用HSV彩色图像分割技术对感兴趣区域（Region of interest,ROI）提取HSV颜色空间分量特征,构建分割指数（Segmentation index,SI）检索得到茶毫、茶身和阴影的最佳分割阈值,采用掩膜法和像素点判别对图像分割效果进行定性和定量评价,并构建茶毫比例量化方法。结果表明,茶毫、茶身和阴影区域的平均分割准确率达到了98.70%,进一步通过茶毫比例量化结果获得祁门红茶3个茶毫品质等级（“显毫”“多毫”和“少毫”）的推荐毫量比例阈值。不同毫量梯度拼配茶样的线性回归分析（R²=0.958,P<0.01）及滇红、金骏眉的泛化应用效果表明,构建的茶毫品质数字化评价方法在不同毫量区间和不同红茶类别上具有较好的适应性。

关键词: 红茶, 感兴趣区域, 茶毫, HSV颜色空间, 图像分割, 阈值

Abstract: Tea hairs are an important indicator for evaluating the appearance quality of black tea. Currently, the evaluation mainly relies on the sensory assessment of professionals, which is highly subjective and abstract, lacking objective and digital quality evaluation methods. To construct a digital evaluation method for the quality of tea hairs, this study collected images of Qimen black tea samples with three different tea hairs quality grades. The HSV color image segmentation technique was used to extract the HSV color space component features of the region of interest (ROI), and a segmentation index (SI) was constructed to retrieve the optimal segmentation thresholds for tea hairs, tea body, and shadows. The image segmentation effect was qualitatively and quantitatively evaluated using the masking method and pixel point discrimination, and a quantification method for the proportion of tea hairs was established. The results show that the average segmentation accuracy of tea hairs, tea body, and shadow areas reached 98.70%. Furthermore, through the quantification of the proportion of tea hairs, the recommended hairs proportion thresholds for the three tea hairs quality grades of Qimen black tea (“Prominent golden hairs”, “Observable hairs”, and “Less hairs”) were obtained. The linear regression analysis of different hairs gradient blended tea samples (R²=0.958, P<0.01) and the generalization application effects on ‘Dianhong’ and ‘Jinjunmei’ indicate that the digital evaluation method of tea hairs quality constructed in this study has good adaptability in different hairs quantity intervals and different black tea categories.

Key words: black tea, region of interest (ROI), tea hairs, HSV color space, image segmentation, thresholds

中图分类号:

S571.1
S126

仝晨, 梁秀华, 王周立, 范冬梅, 林招水, 吴小妹, 阙杨战, 金敏丽, 林杰. 基于感兴趣区域图像分割的红茶茶毫品质数字化评价研究[J]. 茶叶科学, 2025, 45(4): 655-670.

TONG Chen, LIANG Xiuhua, WANG Zhouli, FAN Dongmei, LIN Zhaoshui, WU Xiaomei, QUE Yangzhan, JIN Minli, LIN Jie. Digital Evaluation of Black Tea Hairs Quality Based on Region of Interest Image Segmentation[J]. Journal of Tea Science, 2025, 45(4): 655-670.

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