Canopy VIS-NIR spectroscopy and self-learning artificial intelligence for a generalised model of predawn leaf water potential in Vitis vinifera

This paper focuses on predicting predawn leaf water potential through a self-learning artificial intelligence (SL-AI) algorithm, a novel spectral processing algorithm that is based on the search for covariance modes, providing a direct relationship between spectral information and plant constituents. The SL-AI algorithm was applied in a dataset containing 847 observations obtained with a handheld hyperspectral spectroradiometer (400 -1010 nm), structured as: three grapevine cultivars (Touriga Nacional, Touriga Franca and Tinta Barroca), collected in three years (2014, 2015 and 2017), in two test sites in the renowned Douro Wine Region, northeast of Portugal. The Psi(pd) SL-AI quantification was tested both in regressive (R-2 = 0.97, MAPE = 18.30%) and classification (three classes; overall accuracy = 86.27%) approaches, where the radiation absorption spectrum zones of the chlorophylls, xanthophyll and water were identified along the vegetative growth cycle. The dataset was also tested with Artificial Neural Networks with Principal Component Analysis (ANN-PCA) and Partial Least Square (PLS), which presented worse performance when compared to SL-AI in the regressive (ANN-PCA - R-2 = 0.85, MAPE = 43.64%; PLS - R-2 = 0.94, MAPE = 28.76%) and classification (ANN-PCA - overall accuracy: 72.37%; PLS - overall accuracy: 73.79%) approaches. The Psi(pd) modelled with SL-AI demonstrated, through hyperspectral reflectance, a cause-effect of the grapevine's hydric status with the absorbance of bands related to chlorophyll, xanthophylls and water. This cause-effect interaction could be explored to identify cultivars and cultural practices, hydric, heating and lighting stresses. (C) 2022 IAgrE. Published by Elsevier Ltd. All rights reserved.