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Abstract
The most common method for determining wine grape quality characteristics is to perform sample-based laboratory analysis, which can be time-consuming and expensive. In this article, we investigate an alternative approach to predict wine grape quality characteristics by combining machine learning techniques and normalized difference vegetation index (NDVI) data collected at different growth stages with non-destructive methods, such as proximal and remote sensing, that are currently used in precision viticulture (PV). The study involved several sets of high-resolution multispectral data derived from four sources, including two vehicle-mounted crop reflectance sensors, unmanned aerial vehicle (UAV)-acquired data, and Sentinel-2 (S2) archived imagery to estimate grapevine canopy properties at different growth stages. Several data pre-processing techniques were employed, including data quality assessment, data interpolation onto a 100-cell grid (10 × 20 m), and data normalization. By calculating Pearson’s correlation matrix between all variables, initial descriptive statistical analysis was carried out to investigate the relationships between NDVI data from all proximal and remote sensors and the grape quality characteristics in all growth stages. The transformed dataset was then ready and applied to statistical and machine learning algorithms, firstly trained on the data distribution available and then validated and tested, using linear and nonlinear regression models, including ordinary least square (OLS), Theil–Sen, and the Huber regression models and Ensemble Methods based on Decision Trees. Proximal sensors performed better in wine grapes quality parameters prediction in the early season, while remote sensors during later growth stages. The strongest correlations with the sugar content were observed for NDVI data collected with the UAV, Spectrosense+GPS (SS), and the CropCircle (CC), during Berries pea-sized and the Veraison stage, mid-late season with full canopy growth, for both years. UAV and SS data proved to be more accurate in predicting the sugars out of all wine grape quality characteristics, especially during a mid-late season with full canopy growth, in Berries pea-sized and the Veraison growth stages. The best-fitted regressions presented a maximum coefficient of determination (R2) of 0.61.
Highlights
Precision viticulture (PV) is a strategy to manage vineyard variability by utilizing spatiotemporal data and observations, to enhance the oenological potential of a vineyard
We propose an alternative approach to predict wine grape quality characteristics by combining machine learning techniques and normalized difference vegetation index (NDVI) data collected at different growth stages with non-destructive methods, such as proximal and remote sensing, that are currently used in precision viticulture (PV)
The 2019 and 2020 correlation matrices generally indicated good absolute correlations between NDVI data from all four proximal and remote sensors and total soluble solids, the sugar content measured in °Brix, (|r| > 0.50)
Summary
Precision viticulture (PV) is a strategy to manage vineyard variability by utilizing spatiotemporal data and observations, to enhance the oenological potential of a vineyard. New technologies introduced in support of vineyard management allow for the efficiency and quality of production to be improved, and in parallel, minimizing impacts on the environment (Balafoutis et al, 2017). This is relevant in regions, where high wine production quality standards warrant adopting site-specific management practices to increase grape quality and yield. The most common method used in determining wine grape quality characteristics is to perform sample-based laboratory analysis by obtaining the chemical compounds of the grapes, which can be a timeconsuming, complex, and expensive process (Cortez et al, 2009)
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