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 E-Mail ITHACA, N.Y. - 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 record video of 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. Traditional methods for estimating grape cluster numbers are often done manually by workers, who count a subset of clusters on vines and then scale their numbers up to account for the entire vineyard. This strategy is laborious, costly and inaccurate, with average cluster count error rates of up to 24% of actual yields. The new method cuts those maximum average error rates by almost half.

Jonathan jaramilloKirstin petersenSchool of integrative plant science horticulture sectionPrebloom cluster count prediction in vineyardsCollege of engineeringLife sciencesCollege of agricultureCornell universityComputer vision basedPrebloom cluster count predictionJustine vanden heuvelIntegrative plant science horticulture sectionஜொனாதன் ஜராமில்லோகீர்ஸ்தின் பீட்டர்சன்கல்லூரி ஆஃப் பொறியியல்வாழ்க்கை அறிவியல்