Work

Quality evaluation and prediction model construction of machine-picking fresh tea leaves in tea cultivars based on shoot traits

Year: 2026

Type: article

Abstract: Due to labor shortages, machine-picking of premium tea has become a key research focus in the tea industry. This study systematically analyzed the composition of MPL (machine-picking fresh tea leaves) under different 1B2ML (one bud and two or more leaves) ratios in the machine-picking canopy of five tea cultivars, as well as the main shoot traits and MPL composition of 67 tea cultivars, using principal component analysis, correlation analysis, and stepwise multiple linear regression. The results showed that when the 1B2ML ratio in the machine-picking canopy was 55 %-65 %, the quality of MPL was higher, making it more suitable for machine-picking. Both shoot traits and the composition of MPL exhibited significant cultivar diversity, with clear correlations between them. A comprehensive evaluation model for the quality of MPL was constructed. Correlation analysis indicated that tea cultivars with longer shoots, larger leaf angles, and smaller leaf lengths were more suitable for machine-picking. Additionally, stepwise multiple linear regression was used to construct predictive models for 1B2LR (one bud and two leaves rate), LR (single leaf rate), ISR (intact shoot rate), and SSR (standard shoot rate) of MPL quality indicators based on shoot traits. These findings provide a foundation for breeding and applying suitable for machine-picking tea cultivars and offer new insights and methods for early-stage evaluation and selection of suitable cultivars. • The optimal machine-picking period is when the canopy’s 1B2ML ratio reaches 55 %-65 %. • A comprehensive evaluation model for MPL quality was constructed. • Tea cultivars with long shoot, wide leaf angle, and short leaf suit machine-picking. • A prediction model for MPL quality indicators was constructed based on shoot traits.

Source: Industrial Crops and Products

DOI: 10.1016/j.indcrop.2026.122682

Authors: Linlong Ma, Yanli Liu, Dan Cao, Xiaomei Yan, Yeyun Li, Xiaofang Jin

Institutions: Ministry of Agriculture and Rural Affairs, Hubei Academy of Agricultural Sciences, Anhui Agricultural University

Language: English

Topic: Genetics and Plant Breeding

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDG: Zero hunger

Open Access status: gold

Estimated APC paid: $3,720