Large-scale non-destructive crown-level assessment of Ginkgo pigments via hyperspectral and machine learning techniques.

ABSTRACT: The photosynthetic pigments - chlorophyll a (Chl a), chlorophyll b (Chl b), and carotenoids (Car) - in juvenile ginkgo leaves are crucial for growth monitoring as they reflect physiological status and directly influence the biosynthesis of bioactive compounds such as flavonoids and terpene lactones. Traditional pigment measurement methods (acetone/ethanol extraction, SPAD, etc.) are inadequate for large-scale dynamic monitoring and high-throughput phenotyping analysis. To address this, this study developed a non-destructive prediction model for Chl a, Chl b, and Car contents in ginkgo seedlings using hyperspectral imaging combined with machine learning algorithms, which is applicable to seedlings with different genetic backgrounds and at various color development phases. A total of 3,460 s

SUBMITTER: Yang X 

PROVIDER: S-EPMC12533460 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature