Proximal Sensing and Relationships to Soil and Vine Water Status, Yield, and Berry Composition in Ontario Vineyards

Abstract

Proximal sensing technology was developed to overcome many of the restrictions related to satellite- or aircraft-based remote sensing systems. Ground-based proximal sensing systems collect multispectral images in the visible and near-infrared wavebands and calculate vegetation indices, such as the normalized difference vegetation index (NDVI). The objective of this study was to assess the usefulness in viticulture of NDVI measurements acquired by the GreenSeeker™ optical sensor technology and to relate those measurements with grapevine physiological indicators. It was hypothesized that variability in vegetative expression, yield, and plant water status would relate to NDVIs and that differences in grape composition, including phenols and color, would be identified. It was also hypothesized that spatial variability in the study plots would exhibit temporally stable patterns. Results suggested that NDVI successfully established relationships with most variables; positive relationships were exhibited with vine size and yield components, while inverse correlations were demonstrated with phenols in red cultivars and monoterpenes in Riesling. Clustering patterns in NDVI were confirmed by k-means clustering analysis and Moran’s I spatial autocorrelation index. The usefulness of the GreenSeeker proximal sensing tool was confirmed and is indicative of the future applicability of this technology to divide vineyards into subblocks of different productivity.