Artificial intelligence is getting a lot of buzz in agriculture these days — and for good reason. From developing new seed varieties to guiding sprayers across a field, AI tools are being used in more ways than ever before. But experts say it is important for farmers to understand that not all AI works the same way, and no single tool is right for every operation.
Most people have encountered AI through programs like OpenAI’s ChatGPT or Google Gemini. These are known as large language models, or LLMs — systems built to respond to questions and produce human-like writing. In farming, however, AI reaches far beyond those kinds of chatbots.
Today, agricultural AI is being applied in areas including seed genetics, crop protection products, farm machinery, weather forecasting, and data analysis. Many companies in the ag industry believe AI can help farmers apply inputs more precisely, cut down on waste, and boost overall productivity. The catch? AI is only as good as the information it is given.
Kyle Courtney, co-founder of AgriData Co-op, put it plainly: “Everybody’s excited about AI in agriculture, but nobody wants to talk about what we’re feeding it. An algorithm doesn’t know good data from bad — it just trusts what it’s given. If the numbers coming off our planters and combines are sloppy or incomplete, the AI will hand us confident, well-formatted nonsense. On my own operation, I’ve learned the hard way that clean data going in is the whole game. Quality at the source isn’t a technical detail. It’s the foundation everything else stands on.”
Eric Muckenhirn, also a co-founder of AgriData Co-op, echoed that concern. He said the unglamorous first step to using AI on the farm is good data hygiene. “Mislabeled field boundaries, uncalibrated yield monitors, inconsistent variety records – decisions hinge on small margins, and a model trained on sloppy records will produce confident, wrong answers,” he said.
One of the most significant applications of AI in agriculture is in plant breeding and genetics. Researchers can now process enormous volumes of plant data at speeds that were not previously possible. Instead of spending years watching how certain traits play out in a field, scientists can use machine learning models to predict which plant combinations are likely to perform well under specific conditions.
For soybean growers, that means AI can help identify traits tied to drought tolerance, disease resistance, and yield potential — before a single trial plot is even planted.
Companies like Corteva Agriscience and Bayer are putting significant resources into AI-driven breeding programs. These systems can simultaneously analyze field results, weather patterns, soil conditions, and genetic data. The goal is to speed up the development of new seed products and help crops perform better across a variety of environments.
AI is also playing a role in advancing gene-editing tools such as CRISPR. Computer models help scientists predict where genetic edits are most likely to succeed while minimizing unintended changes. Some companies, including Inari Agriculture, are using AI to improve existing plant genetics without introducing foreign DNA — looking for ways to increase yields while reducing water and fertilizer use.
On the equipment side, AI is transforming how farm machinery operates. John Deere has made major investments in AI-powered tools and robotics. Its See & Spray technology uses cameras mounted on sprayers to scan fields in real time, distinguishing weeds from crops through computer vision. The system then applies herbicide only where weeds are detected, rather than across an entire field.
This kind of precision technology has the potential to reduce chemical use, lower operating costs, and lessen environmental impact. It also represents a broader shift in farming — moving from decisions made at the field level toward management at the individual plant level.
Modern planters and combines are also gathering huge amounts of data. Sensors monitor planting depth, soil moisture, population spacing, and yield figures. That information can be combined with weather data and satellite imagery to guide better decisions in future seasons.
Still, AI is not a cure-all.
Because AI learns from data, flawed or incomplete information can lead to flawed recommendations. Some AI systems are even known to produce incorrect outputs — a problem sometimes called “hallucinations.” That is why farmers are encouraged to keep relying on their own experience, trusted agronomic advice, and local knowledge alongside any AI tools they use.
Muckenhirn offered a cautionary note: “The failure mode I can see coming is farmers treating an AI model output (planting date, variable-rate seeding application, decision to spray or not to spray) as an oracle and switching off the intuition built over decades of knowledge of their fields. The right mentality: AI is a second opinion that’s only trustworthy once it’s been checked against what you already know works on your ground. A yield model that’s never been corrected against your actual combine data is a confident guess at best. Your job shifts from ‘decide everything’ to ‘audit the machine’s reasoning against your historical knowledge and context.’”
Data ownership and privacy are also growing concerns. As more equipment and software collect information from fields, farmers are increasingly asking who owns that data, how it is stored, and who can access it.
For soybean producers, AI will likely show up in gradual, practical ways before making any dramatic changes. It may come through seed selection recommendations, more targeted spraying, automated crop scouting, or improved weather forecasting. As with any emerging technology, those who adopt it earliest take on the greatest risk.
The bottom line is education. Farmers do not need to become technology experts to benefit from AI. But having a basic understanding of what these tools can and cannot do helps producers ask better questions and make smarter choices. Much like GPS guidance and precision planting before it, AI in agriculture will likely keep evolving.
For now, experts suggest treating AI as one more tool in the toolbox — valuable in certain situations, limited in others, and most effective when combined with solid management practices and real-world farming experience.
Key AI Terms for Farmers to Know:
Artificial Intelligence (AI): Computer systems that perform tasks normally requiring human thinking. Machine Learning (ML): AI that learns patterns from data and improves over time. Generative AI: AI that creates new content such as text, images, or code. Large Language Model (LLM): AI trained on large amounts of text to answer questions or write content. Predictive Analytics: Using data and models to forecast future outcomes. Deep Learning: Advanced AI that recognizes complex patterns in massive datasets. Natural Language Processing (NLP): AI that helps computers understand human language. Prompt: The question or instruction given to an AI system. Bias: Errors caused by incomplete or unbalanced data. Hallucination: When AI generates incorrect or misleading information.
