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ISHS Acta Horticulturae 1188: X International Symposium on Grapevine Physiology and Biotechnology

Application of remote sensing by unmanned aerial vehicles to map variability in Ontario 'Riesling' and 'Cabernet franc' vineyards

Authors:   A.G. Reynolds, R. Brown, M. Jollineau, A. Shemrock, E. Kotsaki, H.-S. Lee, W. Zheng
Keywords:   precision viticulture, drones, leaf water potential, soil moisture
DOI:   10.17660/ActaHortic.2017.1188.10
The objective of this investigation was to verify usefulness of proximal sensing technology and unmanned aerial vehicles (UAVs) for mapping variables e.g., vine size (potential vigor), soil and vine water status, yield, fruit composition, and virus incidence in vineyards. Twelve Niagara Peninsula sites (six each of 'Riesling' and 'Cabernet franc') were chosen in 2015. Data were collected from a grid of vines (≈ 80 vineyard-1) geolocated by GPS. Soil moisture and leaf water potential (ψ) data (three times during the growing season; June to September) and yield components/berry composition were collected. Ground based GreenSeekerTM data were likewise acquired June to September, while multi-spectral UAV data were obtained at veraison and processed into geo-referenced high spatial resolution maps of biophysical indices (e.g., NDVI). Following harvest, yield/berry composition maps were also prepared. These data layers in conjunction with growing/dormant season sentinel vine data [e.g., soil moisture, leaf ψ, vine size, winter hardiness (LT50)], were used for map creation. Vine size, LT50, berry weight, and berry composition data were correlated in several vineyards to NDVI and other data acquired with the UAV and GreenSeekerTM, while soil and vine water status, and yield components showed direct relationships with NDVI. Spatial relationships were also apparent from examination of the maps. Principal components analysis confirmed these relationships. Map analysis to determine spatial relationships was accomplished by calculation of Moran's I and k-means clustering. NDVI values were considerably higher in GreenSeeker maps vs. those from UAV flights. Water status zones, and those of several fruit composition variables, were correlated with UAV-derived NDVI. Preliminary conclusions suggest that UAVs have significant potential to identify zones of superior fruit composition.

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