Canopy VIS-NIR spectroscopy and self-learning artificial intelligence for a generalised model of predawn leaf water potential in Vitis vinifera. (July 2022)

• Record Type: Journal Article
• Authors: Tosin, Renan; Martins, Rui; Pôças, Isabel; Cunha, Mario
• Is Part Of: Biosystems engineering. Volume 219(2022)
• Page Start: 235
• Page End: 258
• Publication Date: 2022-07
• Subjects: Hydric potential -- self-learning -- spectroscopy -- plant stress diagnosis; Bioengineering -- Periodicals; Agricultural engineering -- Periodicals; Biological systems -- Periodicals; Génie rural -- Périodiques; Systèmes biologiques -- Périodiques; 631
• DOI: 10.1016/j.biosystemseng.2022.05.007
• Languages: English
• ISSNs: 1537-5110

Abstract

Abstract : 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 Ψpd 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 Ψpd 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. Graphical abstract: Image 1 Highlights: VIS-NIR spectroscopy was used to predict predawn leaf water potential (Ψpd ). Benchmark of SL-AI against state-of-the-art chemometrics methods in modelling Ψpd . Chlorophyll, xanthophyll, and water absorbance bands related to the Ψpd . Cause-effect of plant response to abiotic stresses explained through VIS-NIR bands.