Monitoring the photosynthetic performance of grape leaves using a hyperspectral-based machine learning model

Abstract

Photosynthesis is a direct expression of the crop growth status and an important indicator predicting yield and quality. Rapid and accurate monitoring of the dynamics of photosynthetic is key to field management. In this study, we obtained photosynthetic pigments and water status parameters at the leaf scale during different growth periods of grape. The potential maximum quantum yield (Fv/Fm) of photosystem II (PSII) under dark adaptation and the light response curve (LRC) of the PSII electron transfer rate (ETR) under light adaptation were measured using a pulse amplitude modulated (PAM) chlorophyll fluorometer, while leaf spectral information was recorded using a hyperspectral imager. The maximum ETR (ETRmax) and initial quantum efficiency (kα) were calculated using the LRC model. A Bayesian neural network (BNN) model (implemented in Tensorfolw2.8) was developed to predict Fv/Fm, ETRmax and kα by quantifying the spectral response indices of photosynthetic pigments and water status parameters. A comparison was made with the partial least squares (PLS) and photochemical reflectance index (PRI). The results show that BNN, PLS model and PRI have better predictive performance for Fv/Fm than ETRmax and kα. Compared with the PLS and PRI, the BNN model was able to significantly improve the prediction accuracy, where the validation results for Fv/Fm were R2 of 0.78, ETRmax of 0.57 and kα of 0.53. In addition, the importance of the BNN model input features varied with Fv/Fm, ETRmax and kα, with the vegetation index describing the photosynthetic pigments having the highest importance. The PRI had the worst predictive performance probably because the de-epoxidation state of the xanthophyll cycle pigments is strongly influenced by temporal changes. The model developed in this paper for monitoring photosynthetic performance parameters in grape leaves can simplify the complex photosynthetic reaction process, expand the application of PAM technology and provide a method for rapid and accurate monitoring of photosynthetic performance.