JUNE 2026  SEEDWORLD.COM /  31
He credits team members with automating that workflow.
Kutugata notes that agriculture has lacked the large, well-
labeled image datasets common in other industries. “AgIR 
closes that gap so we can train models that hold up across 
farms, seasons and applications,” he says. Because the dataset 
will remain open, researchers, students, small labs and growers 
can build and test tools without starting from scratch.
The Power and Limits of Big Datasets
Reberg-Horton says the AI crop community needs these 
datasets. “Many other sectors have massive free datasets, but 
agriculture has moved more slowly,” he explains. “The varia­
tion in biological systems makes the problem more complex. 
A stop sign looks similar across locations, but plants vary 
widely even within the same species. That means we need far 
more images.”
He adds that progress will depend on how quickly 
researchers share data. “We hope this effort snowballs until 
teams can train AI systems without always starting with image 
collection.”
Cost Barriers Remain
Washburn, a USDA scientist involved in one of the 2025 
reviews, says cost will continue to limit adoption.
“Both data generation and model development require signif­
icant investment,” he says. “Breeding programs, especially those 
focused on minor crops or operating in the public sector, often 
face tight resource constraints. Many proven breeding methods 
remain underused because of cost.”
Beyond cost, Washburn points to data availability as the 
primary barrier. While datasets such as AgIR will help, many pro­
grams still lack the scale of data needed to deploy AI effectively.
Smarter Models With Less Data
At the same time, he sees opportunity on the technical side. 
“Some of the most important advances will come from meth­
ods that perform well with less data,” he says. 
Techniques such as data augmentation and transfer learn­
ing already support many of today’s leading AI systems. Future 
improvements in this area could expand access and improve 
performance across breeding programs. SW
At the University of Illinois Urbana-Champaign, researchers 
developed an AI vision system that identifies flowering traits 
in miscanthus using drone imagery. Their ESGAN model 
detects heading across thousands of plots and performs 
analysis up to nine times faster than manual field scoring. 
PHOTO: UIUC

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