Mapping Cabernet Franc vineyards by unmanned aerial vehicles (UAVs) for variability in vegetation indices, water status, and virus titer

¹ Cool Climate Oenology and Viticulture Institute, Brock University, St. Catharines, Ontario, Canada
² School of Engineering, University of Guelph, Guelph, Ontario
³ Dept. of Geography, Brock University, St. Catharines, Ontario
⁴ Air-Tech Solutions, Kingston, Ontario
⁵ Dept. of Agriculture and Food, University of La Rioja, Logroño, La Rioja, Spain
⁶ Agro-Paris-Tech, Paris, France
⁷ Dept. of Cell and Molecular Biology, University of Guelph.

Abstract

The hypothesis of this research was that the maps based on remotely-sensed images would create zones of different vigor, yield, water status, winter hardiness and berry composition and the wines from the unique zones would show different chemical and sensorial profiles. A second hypothesis was that titer of grapevine leafroll-associated virus (GLRaV) could be correlated spatially to NDVI and other spectral indices. To determine zonation, unmanned aerial vehicles (UAVs) with multispectral and thermal sensors were flown over six Cabernet Franc vineyard blocks in Ontario, Canada. Zonation was based on NDVI values, and spatial correlations were examined between the NDVI and leaf water potential (Ψ), soil water content (SWC), stomatal conductance (g_s), winter hardiness (LT₅₀), vine size, yield, and berry composition. Additional NDVI data were acquired using GreenSeeker (proximal sensing), and both NDVI data sets produced maps of similar configuration. Several direct correlations were found between UAV-based NDVI and vine size, berry weight, yield, titratable acidity, SWC, leaf Ψ, g_s, and NDVI from GreenSeeker. Inverse correlations included thermal data, Brix, color/ anthocyanins/ phenols, and LT₅₀. The pattern of UAV-based NDVI and other variables corresponded to the PCA results. Thermal scan and GreenSeeker were useful tools for mapping variability in water status, yield components, and berry composition. In 2016, zoned maps were created based on UAV NDVI data, and grapes were harvested according to the separate zones. Additionally, spatial correlations between GLRaV titer and NDVI were observed. Use of UAVs may be able to delineate zones of differing vine size, yield components, and berry composition, as well as areas of different virus status and winter hardiness.