A Double Check: Why Farmers Still Need to Check the Numbers Behind AI Advice
- Joshua Brock
- 2 hours ago
- 6 min read
Artificial intelligence—often called AI—is no longer limited to computers and spreadsheets. It is now embedded in many of the tools farmers use every day, from market analysis and farm financial software to drones flying fields and machines operating autonomously. For some producers, especially those who didn’t grow up with digital technology, this rapid expansion can feel overwhelming.
AI brings real advantages to agriculture. It can process massive amounts of data, recognize patterns faster than a human ever could, and support more informed decision-making. However, one principle remains critical:Â AI is a support tool, not a replacement for farmer experience, judgment, or responsibility.
Whether AI is generating a market forecast, steering a tractor, or identifying disease pressure in a field, it relies on data and assumptions. When those inputs are incomplete, outdated, or misunderstood, the results can lead farmers in the wrong direction. Understanding where AI is used—and how to verify its output—is essential for managing risk.
In this article, we'll take a look at the benefits of incorporating AI into your farm's operations, along with some caveats and things to consider:
Artificial Intelligence in Agriculture: A Growing Presence on Today’s Farms
Artificial intelligence (AI) is no longer limited to computers and spreadsheets. It is now embedded in many of the tools farmers use every day, from market analysis and farm financial software to drones flying fields and machines operating autonomously. For some producers, especially those who didn’t grow up with digital technology, this rapid expansion of technology can feel overwhelming.
AI brings real advantages to agriculture. It can process massive amounts of data, recognize patterns faster than a human ever could, and support more informed decision-making. However, one thing remains critically clear:Â AI is a support tool, not a replacement for farmer experience, judgment, or responsibility.
Whether AI is generating a market forecast, steering a tractor, or identifying disease pressure in a field, it relies on data and assumptions. When those inputs are incomplete, outdated, or misunderstood, the results can lead farmers in the wrong direction. Understanding where AI is used and how to verify its output is essential for managing risk.
AI in Farm Management: Think of It Like a Skilled, but Inexperienced Employee
A helpful way to think about artificial intelligence is to compare it to a highly skilled new employee. AI is fast, efficient, and capable of handling complex tasks quickly. It can analyze prices, monitor equipment performance, review financial records, and alert farmers to potential issues.
At the same time, AI has never farmed your ground, cared for your livestock, or experienced the consequences of a bad year. It does not understand the nuances of your farming operation, your risk tolerance or aversion, or the local factors that influence success. AI systems also tend to present information confidently, even when uncertainty exists.
Just as farmers supervise new employees, AI-generated insights should always be reviewed, questioned, and validated before being used to make important decisions.
Agricultural Markets, Weather, and Farm Financial Analysis
AI is commonly used to analyze agricultural markets by identifying price trends, export demand, and seasonal patterns. These insights can be useful for understanding the broader market environment, but they are often based on national or global data. Local basis, transportation costs, and regional supply-and-demand factors may not be fully reflected.
Weather and climate tools powered by AI can help farmers assess long-term trends such as drought risk or changing planting windows. However, these tools cannot account for field-specific conditions, soil variability, or localized weather events that experienced farmers recognize immediately.
Farm financial analysis is another area where AI is becoming more common. AI-powered tools can organize income and expenses, highlight trends, and flag potential cash flow concerns. However, incorrect acreage, miscategorized expenses, or inaccurate yield data can quickly lead to misleading conclusions. The cleaner and more accurate the farm records, the more useful AI becomes.
It is truly a practical example of the age-old saying, "Garbage in, garbage out!"
Beyond the Office: How AI Is Changing Field Operations
AI is no longer confined to reports and dashboards. It is increasingly present in the field, operating equipment and collecting data in real time.
Drones and Aerial Imaging
AI-powered drones are used to capture high-resolution images of fields, helping identify crop stress, nutrient deficiencies, or drainage problems. While these images can reveal patterns across large areas quickly, they still require farmer interpretation. A stressed area on a map may have multiple causes that only local knowledge can confirm.
Smart Sensors and Connected Equipment
Soil moisture sensors, weather stations, and equipment sensors use AI to monitor conditions and trigger alerts. These tools can help optimize irrigation, reduce fuel use, and identify mechanical issues early. However, sensors only measure what they are designed to measure, and faulty placement or calibration can lead to misleading readings.
Autonomous Tractors and Machinery
Autonomous and semi-autonomous tractors rely on AI to navigate fields, follow routes, and perform tasks with minimal human interaction. While these systems can improve efficiency and reduce labor demands, they depend on accurate mapping, calibration, and oversight. Farmers still play a critical role in setup, monitoring, and decision-making.
Robotics, Crop Protection, and Yield Optimization
AI is also driving innovation in robotics and crop protection.
Robotic Harvesters and Weeding Robots
Robotic harvesters and weeding machines use AI to identify crops, weeds, and harvest-ready produce. These tools can reduce labor needs and improve precision, but they must be evaluated regularly to ensure they are performing correctly under changing field conditions.
Pest and Disease Detection
AI-powered systems can analyze images and data to predict pest pressure or disease outbreaks. These tools can provide early warnings, but predictions are based on probability—not certainty. Field scouting and farmer judgment remain essential before acting on recommendations.
AI in Controlled and Indoor Farming Systems
In indoor and vertical farming operations, AI is used to manage lighting, temperature, humidity, and nutrient delivery. These systems rely heavily on sensors and algorithms to optimize growing conditions. While AI can fine-tune environments efficiently, inaccurate sensor data or system failures can quickly impact production. Human oversight remains a must!
The Risk of Confidently Wrong Information in AI-Driven Agriculture
Across all these applications, from financial, operational, and mechanical, AI shares a common risk: it rarely communicates uncertainty. AI outputs often appear precise and authoritative, even when based on assumptions or incomplete data.
When AI relies on averages instead of local conditions, fills in missing information, or misinterprets sensor data, the results can lead to poor decisions. This is why accurate farm records, regular monitoring, and farmer involvement are essential.
Best Practices for Farmers Using AI Tools Safely
Farmers do not need to be technology experts to use AI responsibly. What matters most is maintaining accurate records, understanding what each tool is designed to do, and verifying critical outputs before taking action.
Checking a few key numbers, reviewing assumptions, and comparing AI insights with real-world observations can prevent costly mistakes. AI works best when it supports and works in tandem with, not replacing, farmer knowledge.
Using AI to Support Better Decisions, Not Make Them
Farm management software plays a key role in successful AI use by keeping financials, production data, and inventories organized and up to date. When records are accurate, AI tools can provide more meaningful insights and help farmers spot trends earlier.
Without solid records, even the most advanced AI tools will struggle to deliver reliable results.
AI is becoming an integral part of modern agriculture, from drones and robots to financial analysis and market forecasting. Used thoughtfully, it can improve efficiency, reduce workload, and support better planning.
But AI does not replace experience, responsibility, or accountability. Farmers still make the final decisions—and live with the results. The most successful operations will be those that use AI as a powerful assistant while continuing to verify numbers, question assumptions, and trust their firsthand knowledge.
In agriculture, as always, the best outcomes come from combining good tools with good judgment.
Joshua, his wife Jenn, and their dog Rooster live in North central Pennsylvania. Joshua is the owner and operator of Hoffman Appalachian Farm, where they grow Certified Naturally Grown hops. Joshua has over twelve years of experience in growing crops, including growing in an organic system. In his spare time, he enjoys trail running, backpacking, and cycling.