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