Syngenta executives say artificial intelligence (AI) is already embedded in tools U.S. farmers use today and is poised to shorten the long wait for new crop protection products, even as it raises the bar on safety, sustainability and performance.
Speaking on an International Federation of Agricultural Journalists webinar, Martin Clough, head of crop protection R&D digital collaboration and sustainability at Syngenta, says a convergence of big data, AI and advanced biology is reshaping how the company discovers and designs inputs.
Clough says the industry-standard 10- to 15-year path from first idea to commercial crop protection product launch no longer fits the pace of change on U.S. farms, especially as weed resistance, government regulation and climate pressure stack up.
From Linear Development to Simultaneous Optimization
Historically, Clough notes that Syngenta — and other crop protection companies, including BASF, Bayer and Corteva — would discover a promising “lead” molecule, then optimize it in long, linear stages: first efficacy, then safety, then cost of goods, then sustainability. Each stage meant thousands of closely related compounds and years of field and lab work.
Now, Clough says, generative design and predictive models let Syngenta optimize many traits at one time.
The company uses roughly 50 AI models to balance about 15 parameters simultaneously, from biological performance to environmental profile and formulation behavior. Behind those models sits a proprietary archive of about a half-million field trials dating back to the 1970s.
Corteva reports that AI has revolutionized its “new product discovery” by allowing the company to “trade randomness and chance for prediction, specificity and design,” according to Brian Lutz, vice president of agricultural solutions for Corteva.
“We can now model proteins and molecules with unprecedented speed and accuracy, allowing us to search through more parts of the chemical universe,” he testified before the U.S. House of Representatives in May 2025.
“For example, we recently used AI to model how 10,000 different molecules might be used in crop protection, all within a matter of weeks,” he noted. “This model was able to identify dozens of new potential crop protection molecules that our chemists could not have found otherwise. We are currently testing these now.”
BASF says it is implementing AI across the company to create value. “We focus on broad adoption of everyday AI tools, push scalable AI value cases in all domains and drive AI innovations to translate the latest AI developments into tangible solutions,” the company reports on its website.
Data Quality Drives AI Performance
“AI is nothing without data,” Clough says, adding that richer, more objective field measurements are now feeding the system.
Hyperspectral imagery — capturing wavelengths beyond visible light — helps detect subtle changes in crop vigor, nutrient response or soil-health effects that a human trial might miss. Those signals are then looped back into the models to improve the next round of designs.
Clough says products fully born from this AI-intensive workflow are still five to 10 years from the marketplace, but the intent is clear: give U.S. farmers safer, more targeted tools that reach them faster than the traditional pipeline allows, and design them around emerging problems in the field rather than yesterday’s pressures.
Connecting Farmer Fields to R&D Scientists
A key piece of that push is tightening the feedback loop between farmers’ fields and Syngenta’s scientists.
Clough says the company is working to connect on-farm performance data directly to R&D while respecting data privacy and farmer consent. He anticipates that better visibility into how products perform in real-world, conservation-leaning systems will shape the next generation of chemistry and biologicals.
That includes tools aimed at regenerative agriculture and soil health, such as nutrient-use-efficiency products or soil amendments that build biomass. Because effects are often subtle and long-term, image-based analytics and AI-driven pattern recognition are central to documenting responses and predicting performance across environments.
Clough frames this as “agricultural intelligence” rather than just artificial intelligence, with the goal of turning farmer experience and field variability into design constraints for the lab. In essence, the scientists must design their new tools and technologies to fit real-world farming conditions from the very start.
AI Already Embedded in Cropwise Platform
On the commercial side, Andre Pisa, global head of digital ag tech for Syngenta, says AI is already woven through the company’s Cropwise digital platform, which covers more than 76 million audited hectares (188 million acres) worldwide.
Cropwise began as a set of standalone apps before evolving into an integrated platform that connects season-long decisions from planning to harvest. Pisa says the current phase layers AI on top of that infrastructure to support faster, more tailored decisions.
“In Cropwise, we are investing heavily in AI, so we can go from the platform and solution journey to decision making and support to the grower,” he says.
Customized Reports for Different Farm Roles
One example is a real-time report that changes depending on who is looking at it.
“If you are the owner of the farm, you will see a certain level of information, but the same report to the manager will be more tactical and more operational, and the same report to the farm worker will be more directed to what you need to apply or you need to do,” Pisa explains. “We are customizing Cropwise as a whole with AI.”
Pisa says AI-driven tools on the platform help with risk mitigation, precision input use and operational planning — from route optimization for machinery to labor scheduling — while keeping local regulations and sustainability goals in view.
Democratizing Access to Agronomic Expertise
Pisa says that one of AI’s largest impacts will be opening access to agronomic expertise, whether for smallholders overseas or large-scale producers in the U.S.
He points to a Syngenta mobile tool used heavily in India and across Asia-Pacific that has more than 4 million downloads and delivers agronomic guidance in thousands of languages. Farmers can upload simple prompts and photos and receive agronomy-informed responses.
“It doesn’t require you to be an agronomist or to be an engineer,” he says. “You just need to show what you’re seeing and ask for results.”
For large, tech-heavy farms, Pisa says AI is becoming a way to interrogate their own data and drive new efficiencies through custom connections into Cropwise.
He believes friendlier interfaces — chatbots, customized reports and natural-language queries — will make complex systems more usable for all farmers.
“I have an opinion that AI will democratize how they grow the access to the technology,” he says.
AI Augments People, Doesn’t Replace Them
Both executives contend that AI augments people rather than replaces them.
“We don’t delegate stuff to AI,” Clough says. “We have scientists and humans working with AI to make sure that we’re using it the right way, the right AI to do the right thing responsibly.”
For U.S. farmers, the bottom line is that AI is already shaping the digital tools they see today and the crop protection products they will see tomorrow — promising a shorter, tighter loop between the problems they face in the field and the solutions coming out of the lab.
