Remote Detection of Pest Damage and Tree Health in Tart Cherry

With the increasing affordability of technology, using technological advancements to assess tree health might soon become a possibility in low input crops such as tart cherry. We evaluated the ability of drone-mounted multispectral cameras, ceptometry (canopy density), and soil conductivity meters (soil texture) to predict pest occurrence and remotely detect tree health. Pest scouting data were compared to the different technological datasets to identify the possibility of use for pest detection. While drone-mounted multispectral cameras were able to identify chlorotic trees, powdery mildew was not reliably detectable under a management threshold with current multispectral camera technology. Trees with low canopy density were found to have decreased powdery mildew, but no other relationship between powdery mildew and canopy density was found in this study. In orchards where soil had higher clay content, canopy density and overall tree vigor was found to be reduced. The variability in pest occurrence, soil texture, canopy density, and overall tree health captured in this study reinforces the challenges of managing orchards. Reducing this variability through variable rate management could enhance cost effectiveness, efficiency, and profitability.