UF/IFAS researchers are finding that AI technology is vastly superior at calculating citrus yield predictions when compared to traditional methods.

UF/IFAS researchers are testing AI technology to help growers with citrus yield predictions, and the preliminary results are that the AI tech is vastly superior to conventional yield prediction methods, according to a Citrus Industry article. Yiannis Ampatzidis, a UF/IFAS associate professor of agricultural and biological engineering and faculty member at the Southwest Florida Research and Education Center, discussed the preliminary results of the research in the article. See the details below.

AI and Citrus Yield Predictions

Researchers have plugged data gathered by unmanned aerial vehicles (UAVs, or drones) taking multispectral images and “manually gathered ground-based data” into a citrus yield-prediction model. Tested during the 2019-2020 citrus harvest season, the model made yield predictions with 98% accuracy, according to the article.

Traditional methods of creating yield predictions generally include growers manually counting fruit from random trees and plugging those numbers into simple mathematical models to create yield predictions for a block of citrus. According to the article, traditional methods have an accuracy of 75% to 85%.

“Citrus yield predictions give growers, packinghouses, and other distributors critical information before the farmers harvest the fruit. Such predictions help growers know what resources such as workers, storage and transportation will be needed for the harvest,” Ampatzidis said in the article. The preliminary results of the research so far were recently presented to the American Society of Agricultural and Biological Engineers.

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