Application of a hyperspectral imaging system to quantify leaf-scale chlorophyll, nitrogen and chlorophyll...

Rapidly and accurately monitoring the physiological and biochemical parameters of grape leaves is the key to controlling the quality of wine grapes. In this study, a Pika L hyperspectral imaging system (400-1000 nm) was used to acquire hyperspectral image information from grape leaves. New vegetation indices were developed on the basis of the screened sensitive wavebands to quantitatively predict changes in these parameters (the leaf chlorophyll level (SPAD), leaf nitrogen content (LNC) and chlorophyll fluorescence parameters (ChlF parameters)). The results showed that SPAD reached its maximum at the grape turning stage and declined thereafter. The vegetation index (D₇₃₅-D₅₇₃)/(D₇₃₅+D₅₇₃) was able to predict SPAD fairly well (validation dataset R² = 0.50). LNC reached its maximum at the grape maturity stage. D₆₈₂/R₅₂₅ was highly correlated with LNC. Except for NPQ, all ChlF parameters showed a decreasing trend from the fruiting to harvesting stages. Among the dark-adapted ChlF parameters, F_V/F_m had the strongest correlation to the new vegetation index (D₇₃₅-D₅₄₄)/(D₇₃₅+D₅₄₄) (modelling dataset R² = 0.68), and Fo had the weakest correlation. Among the light-adapted ChlF parameters, Y(II) had the strongest correlation to the new vegetation index D₆₇₆/R₅₇₁ (validation dataset R² = 0.63); this index also had good predictive power for Fm' (validation dataset R² = 0.52) but low predictive power for Fo'. All the calculated vegetation indices had weak relationships with NPQ. In addition, this study also verified the predictive abilities of vegetation indices developed in previous studies. This study can provide a technical basis for the nondestructive monitoring of the physiological and biochemical parameters of grape leaves with hyperspectral imaging systems.