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By Agroempresario.com
Artificial intelligence is transforming the global agricultural landscape, dramatically accelerating the discovery and development of crop protection products. That was the central message delivered during a hearing this week before the House Environment Subcommittee, where top executives and academics offered compelling testimony on how AI is reshaping agrichemical innovation.
Brian Lutz, PhD, Vice President of Agricultural Solutions at Corteva Agriscience, told lawmakers that AI is helping the company model proteins and molecules “with unprecedented speed and accuracy.” The result, he said, is a dramatic shift from traditional trial-and-error methods to a future of predictive and precise innovation.
“AI has revolutionized discovery by allowing us to trade randomness and chance for prediction, specificity, and design,” Lutz emphasized.
He explained how crop protection molecules function like keys meant to fit specific protein "locks" in pests such as weeds, insects, or pathogens. Historically, identifying the right protein targets took months and thousands of dollars. Today, Corteva can predict protein structures in seconds using AI for only a few cents per prediction.
“We recently used AI to model how 10,000 different molecules might be used in crop protection, all within a matter of weeks. This model was able to identify dozens of new potential crop protection molecules that our chemists could not have found otherwise,” Lutz said. These molecules are now undergoing further testing.
In addition to synthetic compounds, Corteva is also using AI to accelerate the discovery of biological crop protection products. By predicting the metabolites and biomolecules produced by microbes, AI enables the company to engineer bacterial strains and optimize fermentation processes with remarkable efficiency.
“AI helps us predict the incredible diversity of natural biomolecules,” said Lutz, “and we use that knowledge to design and scale up sustainable biologicals.”
AI isn’t just confined to the lab. Corteva is piloting AI-powered fungicide timing models in the U.S., combining environmental data with farm-specific information to give farmers actionable insights.
“This combined intelligence helps farmers know exactly when to spray to combat key corn diseases,” Lutz said. “We’re moving towards a world where precision, not generalization, defines crop protection.”
Dr. Boris Camiletti from the University of Illinois Urbana-Champaign presented evidence of how AI is being deployed to manage red crown rot, a rapidly spreading fungal disease in soybean crops.
Using high-resolution satellite imagery and machine learning algorithms, Camiletti’s team can detect disease hotspots in fields. “We train the models with visible to near-infrared spectral data and ground-truth observations,” he explained.
These AI-driven models not only detect disease presence but also track its progression, allowing farmers to apply fungicides only in affected zones rather than across entire fields. “It’s a precision agriculture platform that reduces chemical use while improving disease control,” said Camiletti.
Dr. Daniel Swale from the University of Florida warned that the U.S. is falling behind countries like Japan and China in the race for next-generation crop protection products.
“Japan exceeds the U.S. in first-in-class pesticides by more than two-fold,” Swale told the committee. “Meanwhile, the Florida citrus industry, once valued at up to $10 billion, is collapsing due to lack of effective tools against the Asian citrus psyllid.”
Swale’s team is now applying AI technologies to discover naturally occurring chemicals with pesticidal properties. With fewer regulatory barriers than synthetic compounds, natural products may offer a faster path to market. Thanks to partners like Enveda Biosciences in Boulder, Colorado—originally focused on drug discovery—AI models can now analyze complex natural mixtures with unparalleled speed and accuracy.
Despite the promise of AI, Swale cautioned that its effectiveness is only as good as the data used to train it. “Currently, the inputs needed for agrochemical discovery remain poorly understood due to the lack of public data,” he said.
Private companies traditionally guard their datasets for intellectual property reasons, creating gaps that hinder academic research and AI modeling. Swale argued that universities, supported by federal funding, should play a central role in agrochemical discovery by creating open-access datasets.
However, this vision could be jeopardized by cuts to federal research funding. Congressman Gabe Amo (D-Rhode Island) warned that the Trump administration has paused billions in federal grants and is proposing a 55% cut to the National Science Foundation’s budget.
“These are the very dollars that support AI development in agriculture,” Amo said. “Without accurate and transparent climate and field data, no algorithm can guide a farmer effectively.”
Lutz from Corteva echoed this concern. “Our fungicide timing models rely heavily on publicly available climate data. If those data streams are disrupted, so is our ability to innovate.”
Amo added: “Without these data, farmers can’t plan. The foundation of AI is science and data—and that foundation is crumbling.”
