Cornell University engineers and plant scientists have teamed up to develop a low-cost system that allows grape growers to predict their yields much earlier in the season and more accurately than costly traditional methods.
The new method allows a grower to use a smartphone to video grape vines while driving a tractor or walking through the vineyard at night. Growers may then upload their video to a server to process the data. The system relies on computer-vision to improve the reliability of yield estimates.
The traditional method is laborious, costly and inaccurate, with average cluster count error rates of up to 24 percent of actual yields. The new method cuts those maximum average error rates by almost half. The project is funded in part by USDA’s National Institute of Food and Agriculture. For more information, read the Cornell Chronicle article.