Investigating a Selection of Methods for the Prediction of Total Soluble Solids Among Wine Grape Quality Characteristics Using Normalized Difference Vegetation Index Data From Proximal and Remote Sensing - Kasimati, Aikaterini; Espejo-Garcia, Borja; Vali, Eleanna; Malounas, Ioannis; Fountas, Spyros (Frontiers in Plant Science, 2021)
Currently, the most common method used in determining wine grape quality characteristics is to perform sample-based laboratory analysis, which can be a very time-consuming and expensive process. In this paper, we propose an alternative approach to predict wine grape quality characteristics in a robust way, 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. The study involved several sets of high-resolution multispectral data derived from four sources, including two vehicle-mounted crop reflectance sensors, UAV-acquired data and Sentinel-2 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 x 20 m) and data normalization. Initial descriptive statistical analysis, by calculating the Pearson’s correlation matrix between all variables, 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 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, and the CropCircle, during Berries pea-sized and the Veraison stage, mid-late season with full canopy growth, for both years. UAV and Spectrosense+GPS data proved to be more accurate in terms of predicting the sugars out of all wine grape quality characteristics, especially during 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.