38  / SEEDWORLD.COM  OCTOBER 2026
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ades of siloed data, from R&D notebooks 
to breeding data and agronomy notes 
all the way to sales data. Yes, you need 
a special team that understands how 
to navigate data in these complicated 
environments, to access and organize 
and convert the data into a form that AI 
can use, but there would be a lot of value 
in having all a breeding company’s data 
analyzed by AI for a variety of purposes.”
AI could also have a large impact on 
automating the processing of breeding 
data, says Dr. Jacob Washburn, super­
visory research plant geneticist for the 
Midwest Area at USDA’s Agricultural 
Research Service.
at different scales. Some need to “under­
stand” the genomic and environmental 
context of field trials around the globe, 
while others allow researchers to target 
specific genes or base pairs in a given 
variety.
“This allows you to optimize very com­
plex traits like yield that are controlled 
by thousands of genes as well as identify 
causative genes for ‘simple’ traits,” he 
says. “These are traits controlled by a few 
or a few dozen genes and if you try to 
take the whole genome approach, you'll 
get a lot of unintended consequences 
that take decades to fix. Corn ear number, 
for example, can be optimized by focus­
ing on just a few genes.”
Better AI Still Depends on 
Better Data
The usefulness of AI in breeding depends 
heavily on the quantity, quality and diver­
sity of the data behind it.
Alvarez and others expect future 
progress to rely on combining drone-
derived phenotype data with genomic, 
transcriptomic and metabolomic data 
collected across multiple environments 
and seasons.
“Creating large, complex data ‘fly­
wheels’ is the future of breeding,” Alverez 
says, “but they have to be scalable, and 
the data streams are only part of the pic­
ture. They have to be cheap and interpret­
able data streams to be truly impactful.”
Washburn agrees that data availability 
will help determine how useful AI tools 
become and which breeding programs 
can adopt them.
“Large companies, breeding networks 
and other groups that are able to aggre­
gate large amounts of data will be able to 
leverage the newest AI methods the fast­
est and to the greatest benefit,” he notes. 
“At the same time, innovations that allow 
AI models to perform well with less data 
will be critical to enabling the use of AI 
in breeding, especially for public breed­
ing programs, and programs focused on 
crops with less use/economic impact.”
That creates a particular challenge for 
public breeders and programs working in 
“There are still a number of human 
bottlenecks that will likely be increasingly 
automated, some of that likely using AI,” 
he says. “Even some apparently simple 
steps like accurately identifying plots in a 
field and tracking them over successive 
dates still require significant human inter­
vention, at least in the public sector.”
Where Will AI Make the 
Biggest Difference?
The larger question is what those capa­
bilities will ultimately mean for crop 
improvement. Will AI primarily help 
breeders make incremental gains in areas 
such as photosynthetic efficiency, or 
could it contribute to larger advances, 
including perennial versions of annual 
crops or corn and wheat capable of fixing 
nitrogen?
Mariano Alvarez, co-founder and chief 
science officer at Avalo, sees potential for 
both.
“AI's ability to map the broader con­
text of gene interactions across indi­
viduals and populations is where the real 
magic happens,” he says.
On the subject of “huge revolutionary 
achievements,” Alvarez points out that 
many of these traits already exist in the 
wild.
“They are not that ‘revolutionary’ for 
nature,” he explains, “but we have not 
yet been able to harness that power for 
agriculture. Essentially, nature can do 
almost anything, and with AI platforms…
we can start to harness the full potential 
of natural innovation.”
Washburn also sees potential in both 
areas, although he leans toward incre­
mental gains.
“There may be some surprises with 
large breakthroughs that have evaded 
breeders for a long time,” he says. “But 
based on the information we currently 
have, it’s reasonable to anticipate that the 
greatest gains in breeding will continue 
to be through small improvements that 
together add up to significant impacts.”
In Zamft’s view, achieving larger 
advances such as nitrogen fixation in 
staple crops will require models that work 
Avalo co-founder and chief science officer 
Mariano Alvarez sees AI as a tool for 
uncovering complex genetic interactions 
and expanding what breeders can achieve. 
PHOTO: AVALO

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