Scientists Showcase New Tools to Better Predict Strawberry Yields
This image from the new PhenoSnap web tool shows tomatoes detected by the AI model in a research field at the UF/IFAS Gulf Coast Research and Education Center. | Kevin Wang, UF/IFAS
University of Florida researchers are developing web-based tools that incorporate artificial intelligence to help producers with yield predictions.
UF/IFAS Assistant Professor of Agricultural and Biological Engineering Kevin Wang gave strawberry growers an update on the two-step applications, known as PhenoSeg and PhenoSnap, at the recently held AgriTech conference in Plant City, Fla.
PhenoSeg focuses on segmenting individual strawberry plants from drone imagery—essentially isolating each strawberry plant from the background so scientists and growers can count plant-level fruit and flowers more precisely.
PhenoSnap is a 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 as well as on two commercial growers’ farms.
“The results so far are encouraging,” Wang said. “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, which can save 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 said.
For additional information on the PhenoSeg and PhenoSnap web-based tools, continue reading on the UF/IFAS website.