HAS THESE last week, executives from agriculture, genetics And crop protection companies during a round table describe how artificial intelligence (AI) is moving upstream into the economic heart of agriculture, reshaping decisions about what is planted and where inputs are applied And how returns are maximized. Agriculture operates on razor-thin margins global on a large scale, meaning even small gains in efficiency or forecasting can ripple into billions of dollars across food supply chains.
THE sign featuring executives John Deere, Inheritable Agriculture And crop protection company Invaio Sciences highlighted how AI is evolving from a set of precision tools to a software layer that bridges genetics, chemistry And machines in the field. Rather than replacing farmers, panelists said, AI absorbs complexity that farmers have historically managed through experience and intuition. And manual labor.
From precision equipment to software-defined agriculture
For decades, innovation in agriculture has focused on equipment: bigger machines, better sensors And stricter mechanical control. AI is now focusing on software-defined agriculture, in which machines execute plans created by models combining satellite imagery and historical yield data. And real-time sensor inputs.
Melissa Neuendorf, who leads AI efforts at John Deere, cited harvesting as a clear example. Modern combine harvesters can now automatically adjust their speed based on expected crop density, maximizing productivity without constant human intervention.
“We are able to predict and understand what the crop density will be,” Neuendorf said. “Then the machine will adjust its speed to maintain optimal productivity. »
The goal, she stressed, is not to turn farmers into technologists. “Farmers didn’t get into farming because they wanted to become data scientists,” Neuendorf said. “They got into farming because they wanted to feed and clothe the world. »
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The same principle applies to weed control. PYMNTS has already covered how John Deere integrated AI into its equipment to monitor changing field conditions.
AI goes upstream into seeds and chemicals
While companies like John Deere focus on field execution, AI is also reshaping decisions made years before planting. Tim Beissingerco-founder and CTO of Heritable Agriculture, describe how AI models help match plant genetics to specific environments and management practices.
“One of the biggest problems in plant genetics is the genotype-environment interaction,” Beissinger said. “We use AI models to determine a particular set of genetics, where is it okay perform best and how is it okay perform best.
Heritable also applies new generative AI techniques to the genomes themselves, treating DNA as a language of base pairs rather than words. “This kind of thing wasn’t possible five years ago,” Beissinger said, emphasizing that this approach accelerates discovery without necessarily requiring genetic modification.
Digital twins, trust and the adoption economy
A recurring theme within the panel was ladder. AI enables decisions to be made with a level of geographic and biological precision that was previously impractical. Beissinger said his team can now “drop a pin anywhere on the planet” and instantly estimate soil parameters and weather conditions that once required weeks of manual sampling.
This capability underpins the rise of digital twins in agriculture. John Deere operating platforms already allow farmers must maintain digital representations of equipment and fields And entries. Heritable uses digital twins of plant varieties to simulate how genetics respond to changes in soil and irrigation Or climate.
“We absolutely can’t do it,” Beissinger said. “Here we are.”
Still, panelists cautioned that adoption depends on trust.
“How do we build trust in automation alongside the human experience?” » » asked Neuendorf. The answer, she suggested, lies in providing clear economic value without overwhelming farmers with data they must interpret for themselves.
