Brent Murphree,Senior Editor,Farm Press July 22, 2026
https://www.farmprogress.com/technology/practical-applications-for-ai-in-farming-continue-developing
AI is a powerful tool for farmers, but data security and recommendation reliability still require extra care.Dmytro Diedov/Getty Images
At a Glance
- Artificial intelligence is becoming more common on farms nationwide.
- Always verify AI-generated recommendations, and use them with caution.
- AI technology will need to be further operationalized before it can become more widespread.
Artificial intelligence is becoming increasingly common on agricultural operations, and farmers can use it to assist in a variety of on-farm tasks.
Aaron Smith, professor of agricultural and resource economics specialization and farm bill policy, and Sathish Samiappan, associate professor of biosystems engineering and soil science, both from the University of Tennessee, presented several examples of how AI is already transforming farms across the Midsouth at the Southern Cotton Ginners Association summer meeting in Florence, Ala.
Production planning
One of the most powerful applications Smith described involved a Kentucky farmer who uses AI to analyze a decade’s worth of production data.
“This report analyzes 10 years of precision planting and harvest data across his corn, soybean [and] wheat on the farm,” Smith said. “It’s based on his John Deere Operation Center precision seeding and harvest records.”
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The AI analysis revealed critical insights that could significantly impact profitability for the grower’s operation, including delayed planting costs and specific management recommendations. Smith emphasized the practical value of such analysis.
“How do you take this and turn it into an actionable decision is where I really see that benefit? It can really help you in terms of doing that analysis,” he said. “It can also help you in terms of saving money or giving you an indication of what the probability of a more profitable outcome is.”
Compliance support
Smith also demonstrated how AI can help with regulatory compliance, especially regarding pesticide applications. Using a simple example, he showed how farmers can upload herbicide labels and ask specific questions about application timing and conditions.
“Based on the attached label requirements and current weather conditions and time of year, can I spray Liberty on cotton?” he asked. “The program said, ‘Yes, you can likely spray today, but I would target an application from now through late morning rather than waiting until the afternoon.’”
The system automatically pulled in weather data and provided justifications based on temperature, sunlight, rain, wind and weed conditions.
However, Smith cautioned about verification. Asking leading questions can result in off-label recommendations, so he cautions users to be very careful and always refer back to the source label or document. This can be done by asking the program to show the exact source of the information.
Cost optimization
Smith shared another practical application involving a farmer using AI to optimize input purchasing decisions.
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“He’s using AI models to end up looking at local retailer information and to work with companies directly to end up seeing if it makes sense when to end up booking his different inputs at different times of the year,” he said.
The farmer’s innovative approach caught industry attention.
“He actually had some of the people from the headquarters at Nutrient reach out to him to end up explaining what he was trying to end up doing,” Smith said. “I think there’s going to be a lot of ability to connect with vendors in terms of where are those sweet spots for purchasing stuff relative to carrying inventory.”
Remote sensing
Samiappan focused on AI applications using imagery from satellites, drones and ground robots. His research looks to make sense of the photos for farmers and use them to recommend future steps, he said.
One breakthrough application involves detecting plant stress before visible symptoms appear.
“We looked at spectral information from cotton leaves, and we were able to detect the infestation of root knot nematodes less than two weeks before any visual symptoms showed up,” Samiappan said. “Knowing this information at an earlier stage will let you decide whether you need to keep putting inputs into your field. So that could lead to a significant cost savings.”
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Samiappan also discussed using AI for wildlife damage assessment.
“You can use drone data to automatically get a report on where this damage is occurring — what’s the total loss,” he said. “And then the model might possibly suggest to you what actions that you could take to prevent this.”
Looking ahead
Smith and Samiappan both noted that AI is still evolving. Samiappan added that while some technologies exist today, AI needs to be operationalized in the coming years for growers’ needs. Smith emphasized the importance of data quality and security, distinguishing between consumer-level AI platforms and enterprise systems that protect proprietary farm data.
As AI continues to develop, both experts see tremendous potential for agriculture. The key, they suggest, is understanding how to use these tools effectively while maintaining appropriate oversight and verification of AI-generated recommendations.