Vineclipper: A Proximal Search Algorithm to Tie Gps Field Locations to High Resolution Grapevine Imagery

Grapevine canopy characteristics as determined from remotely sensed imagery have been shown to be effective in forecasting grape composition parameters that can be used to estimate the quality of wine made from those grapevines. Maps of canopy characteristics are therefore valuable tools for precision viticulture practice. In a case of extracting reflectance data at the scale of individual vines from vineyard imagery with a pixel resolution of ca. 0.5 m, simple use of sample point location data provided by a GPS (the GPS points) projected onto a georectified image proved too inaccurate for the desired analysis. At the individual vine scale, the spatial error between the GPS point and the corresponding location in a georectified image was great enough to result in clearly incorrect pixels being identified as representative of the sample grapevine canopy. The sample GPS point locations were, however, sufficiently close to the correct vine canopy in the georectified image to act as a seed point for a computer search algorithm.