“Citrus yield predictions give growers, packinghouses and other distributors critical information before the farmers harvest the fruit,” said Ampatzidis, a faculty member at the Southwest Florida Research and Education Center. “Such predictions help growers know what resources such as workers, storage and transportation will be needed for the harvest.”
In a preliminary study presented last month to the American Society of Agricultural and Biological Engineers, UF/IFAS researchers showed how they used AI technology to generate two citrus yield-prediction models.
So far, scientists prefer one of those models, which they tested during the 2019-2020 citrus harvest season.
It combines data from unmanned aerial vehicles (also known as UAVs, or drones) with manually gathered ground-based data. Specifically, the technology uses an AI-based model that combines UAV multispectral images with ground-collected color – red, green and blue — images to predict citrus yield.
Some growers also hire consulting companies to predict yield, he said.
The accuracy of these traditional models varies, and it’s often around 75% to 85%.
UF/IFAS researchers used Agroview, a novel cloud-based technology that was named a UF Invention of the Year in 2020, to analyze the multispectral images and to determine tree characteristics, such as height, canopy size, leaf density and health – in addition to the number of fruit.
“We plan to continue this research, collecting more data to further develop and evaluate the model and this yield prediction technique,” Ampatzidis said.
Source : ufl.edu