LAS VEGAS, January 12, 2026 — Artificial intelligence is quietly reshaping the economics of farming, and the change isn’t about robots in the fields—it’s about smarter decisions happening long before planting even begins. That’s the takeaway from discussions at CES last week, where agriculture, genetics, and crop-protection leaders revealed how AI is moving into the core of food production, potentially adding billions to global supply chains.
From Precision Tools to a Software-Defined Future
AI is no longer just about precision equipment; it’s becoming the software layer connecting genetics, chemistry, and machines in the field.
For decades, agricultural innovation focused on hardware—bigger machines, better sensors, and tighter mechanical control. Now, AI is shifting that focus toward software-defined farming, where machines execute plans created by models that combine satellite imagery, historical yield data, and real-time sensor inputs. This isn’t about replacing farmers, but about absorbing the complexity they’ve historically managed through experience and intuition.
Melissa Neuendorf, who leads AI efforts at John Deere, illustrated this with harvesting. Modern combines can now automatically adjust their speed based on predicted crop density, optimizing productivity without constant human intervention. “We’re able to predict and understand what the crop density is going to be coming up,” Neuendorf said. “Then the machine is going to adjust its speed so that it can maintain its optimum productivity.”
The goal, Neuendorf emphasized, isn’t to turn farmers into data scientists. “Farmers didn’t get into farming because they want to be data scientists,” she said. “They got into farming because they wanted to feed and clothe the world.”
AI’s Expanding Role: Seeds and Chemical Applications
While companies like John Deere concentrate on field execution, AI is also influencing decisions made years in advance. Tim Beissinger, co-founder and CTO of Heritable Agriculture, described how AI models help match plant genetics to specific environments and management practices.
“One of the biggest problems in plant genetics is genotype-by-environment interaction,” Beissinger said. “We use AI models to figure out for a particular set of genetics, where will it perform best and how will it perform best.”
Heritable is also applying generative AI techniques to genomes, treating DNA as a language of base pairs. “This sort of thing wasn’t possible five years ago,” Beissinger noted, adding that the approach accelerates discovery without necessarily requiring genetic modification.
Digital Twins and the Importance of Trust
A key theme throughout the panel was the power of scale. AI enables decisions with a level of geographic and biological precision previously impractical. Beissinger explained that 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 supports the rise of digital twins in agriculture. John Deere’s platforms already allow farmers to maintain digital representations of equipment, fields, and inputs. Heritable uses digital twins of plant varieties to simulate how genetics respond to changes in soil, irrigation, or climate.
“We are absolutely not getting there,” Beissinger said. “We’re there.”
However, panelists cautioned that widespread adoption hinges on trust. “How do you build the trust of automation alongside the human experience?” Neuendorf asked. The answer, she suggested, lies in delivering clear economic value without overwhelming farmers with data they can’t readily interpret.
