Scientists Tout Tools To Better Predict Strawberry and Tomato Yields

University of Florida researchers are developing web-based tools that incorporate artificial intelligence to help producers with yield predictions.

The goal of the smart tech tools is to give growers a fast, accurate estimation and prediction of yields, rather than making economic projections based on manually counting the crops or historic data that can vary greatly.

Kevin Wang, a UF/IFAS Assistant Professor of agricultural and biological engineering , gave strawberry growers an update on the two-step applications, known as PhenoSeg and PhenoSnap, at the recently held AgriTech conference in Plant City.

PhenoSnap is the UF web-based application that detects and counts fruit, flowers, and runners on strawberries. It can also count tomato fruit and flowers.

Both applications are hosted on UF’s HiPerGator, the nation’s fastest university-owned supercomputer. Because they’re on HiPerGator, researchers and growers don’t need to install any software or have powerful computers, Wang said. They can get the results by uploading images through a web browser.

During the 2025–2026 growing season, scientists collected drone imagery on the research farm at the UF/IFAS Gulf Coast Research and Education Center in Balm as well as on two commercial growers’ farms.

“The results so far are encouraging,” Wang says. “PhenoSeg’s plant segmentation is performing well. PhenoSnap’s fruit and flower counting still tends to undercount, which is a known limitation we’re actively working to improve in the next phase of development. We want to be transparent about that – the software application tool works, but the vision models and the algorithm supporting the software still need refinement.”

The system works like this: A drone flies over a strawberry field and captures high-resolution color images. Compared to walking rows by hand or using a ground-based scouting platform, drone-based data collection covers far more ground in far less time, saving growers money and time.

After the flight, images are downloaded from the drone camera to a computer and uploaded to PhenoSeg, which handles plant segmentation first. It isolates each individual plant, so the system knows what it’s viewing. Those plant-level images are then uploaded to PhenoSnap, which counts the fruit and flowers on each plant.

“If any growers are interested in trying the tools, we’re very open to that,” he says.

For more, continue reading at blogs.ifas.ufl.edu.