Detecting cool-climate Riesling vineyard variation using unmanned aerial vehicles and proximal sensors

Abstract

The ability to detect and respond to vineyard spatial variation can lead to improved management—a practice known as precision viticulture. The goal of this study was to determine if remote sensors can enhance precision viticulture applications by detecting vineyard spatial variation. The hypothesis was that differences in vine spectral reflectance, as detected by remote sensors, would be associated with variations in viticultural variables due to known relationships with vine size, structure, and pigmentation. Riesling grapevines were geolocated within six commercial vineyards across Niagara, Ontario. Water status, vine size, winter hardiness, virus titer, yield components, and berry composition were measured on these vines. Remote sensing technologies subsequently collected multispectral data by unmanned aerial vehicles and by proximal sensing technology (GreenSeeker™), which were transformed into the Normalized Difference Vegetation Index (NDVI). Direct relationships between NDVI and vine size, water status, yield, berry weight, and titratable acidity were observed, as well as inverse relationships between NDVI and Brix and potentially volatile terpenes. Remote sensing demonstrated the ability to detect vineyard areas differing in measures of vine health, yield, and berry composition in certain sites and years; however, more research is needed to determine when these technologies should be used for precision viticulture applications.

Keywords

• remote sensing
• proximal sensing
• precision viticulture
• unmanned aerial vehicles