Spatial Crop Load Mapping; Data Fusion and Uncertainty

By: Jacqueline Dresser

Spatial Data Tech Group If you have been keeping up with the Efficient Vineyard blog posts, you have learned that vineyards are variable, this variability has economic implications and understanding the variability of a vineyard is the first step toward efficient vineyard management. The spatial data tech group’s priority is to create tools that convert raw spatial data into informative maps that demonstrate the variability in a particular vineyard and help inform management decisions. Lately, we have been spending time streamlining data processing and data fusion techniques to generate maps of crop load accompanied by maps of uncertainty in the crop load values.

Crop load is typically measured in two ways. The first, Ravaz Index, is a ratio between the mass of harvested fruit and the mass of pruned wood in the following dormant season. The second is a ratio between leaf area and fruit mass. In order to generate a crop load map, measurements of both yield and vine size that cover an entire vineyard are needed. To this end, yield monitors, optical reflectance sensors like the CropCircle or Greenseeker, and a bit of manual pruning mass sampling make crop load mapping possible. That is, with a little help from the efficient vineyard team.