Artificial intelligence is quickly becoming one of the most important technologies in modern agriculture. From autonomous tractors and crop scouting apps to livestock monitoring and yield prediction, AI tools are beginning to influence how farmers, agronomists, researchers, and agricultural businesses make decisions across the Midwest.
During the 2026 ISU Soil Management and Land Valuation Conference, speakers explored how artificial intelligence is being used throughout the agricultural industry and why human expertise still plays a critical role. The conference focused on practical applications of AI in farming while also discussing the risks, limitations, and future opportunities tied to the technology.
One of the clearest takeaways from the conference was simple: AI is a tool that can improve efficiency and decision-making, but successful farming still depends on experienced people applying the information correctly.
What Is Artificial Intelligence in Agriculture?
Artificial intelligence in agriculture refers to computer systems and software designed to perform tasks that normally require human intelligence. These tasks can include:
- Identifying weeds and insects
- Predicting crop yields
- Monitoring livestock health
- Analyzing satellite imagery
- Automating machinery
- Organizing research data
- Detecting crop stress
The conference focused heavily on large language models (LLMs) such as OpenAI ChatGPT, Google Gemini, and Claude, along with computer vision systems used for image recognition and field analysis.
Speakers explained that AI technology is evolving rapidly, but many expectations about AI are still shaped by media headlines and popular culture rather than practical agricultural use.
AI Is a Tool — Not a Replacement for Farmers
One speaker compared artificial intelligence to a power drill used by a carpenter. The drill helps complete work faster and more efficiently, but the final result still depends on the skill and experience of the person using the tool.
That same concept applies directly to agriculture.
AI can help farmers and agricultural professionals:
- Analyze large amounts of data
- Automate repetitive tasks
- Improve scouting efficiency
- Organize information
- Detect field problems earlier
- Improve livestock monitoring
However, presenters repeatedly emphasized that the “secret sauce” remains the farmer, agronomist, researcher, or land manager interpreting the results correctly.
The conference also highlighted that AI systems are still imperfect. Artificial intelligence can generate incorrect information, misunderstand context, or produce misleading outputs if human oversight is removed.
How AI Is Already Being Used in Agriculture
The conference showcased several real-world examples of artificial intelligence currently being used throughout agriculture.
Crop Scouting and Field Monitoring
AI-powered crop scouting apps can now identify weeds and insect species from smartphone photos with impressive accuracy.
These tools may help:
- Crop scouts
- Agronomists
- Farmers
- Remote management teams
make faster and more informed herbicide or pesticide decisions.
Researchers also discussed Iowa State University projects using:
- Satellite imagery
- Drone imagery
- Soil data
- Weather data
- Field history
to predict crop maturity, estimate nitrogen levels, monitor crop stress, and forecast yields.
Autonomous Equipment and Precision Agriculture
Artificial intelligence is also expanding rapidly in farm equipment and precision agriculture systems.
Examples discussed during the conference included:
- Autonomous tractors
- Auto-steer technology
- Precision planting systems
- Variable-rate applications
- AI-assisted machinery software
While fully autonomous farming is still developing, many producers already use AI-assisted technologies daily through precision agriculture tools.
The conference also referenced Iowa State University autonomy research being conducted within agricultural engineering programs.
AI Applications in Livestock Production
Livestock operations are increasingly using artificial intelligence and computer vision systems to improve herd management and animal monitoring.
Conference examples included:
- Robotic milking systems
- Feed optimization technology
- Lameness detection in dairy cattle
- Methane prediction models
- Livestock health monitoring
One presentation highlighted how dairy cattle lameness detection improved significantly after farmers suggested including back-arching behavior within the AI system’s analysis. That adjustment reportedly improved accuracy from roughly 60% to more than 90%.
Researchers also discussed AI systems that analyze milk data to estimate methane production and improve future breeding decisions.
Why AI Is Valuable for Agricultural Research
Agriculture generates enormous amounts of data every year.
Conference speakers explained how difficult it has become for researchers and agricultural professionals to keep up with millions of new research papers, datasets, reports, and studies.
AI tools can now help summarize information, identify key findings, and organize research much faster than traditional manual review methods.
One example shared during the conference suggested that a project previously requiring approximately 70 hours of work could potentially be completed in less than 20 minutes when AI tools are combined with human review and quality checks.
Risks and Limitations of AI in Agriculture
Although the conference highlighted many exciting opportunities, speakers also warned attendees about several important limitations tied to artificial intelligence.
AI Can Still Produce Incorrect Information
Artificial intelligence systems do not truly “understand” information like humans do. Instead, they predict responses based on patterns found in data.
As a result, AI systems can sometimes:
- Produce inaccurate answers
- Fabricate information
- Miss local farming conditions
- Repeat incorrect assumptions
- Struggle with ethical decisions
Conference speakers stressed that AI should support agricultural decision-making — not fully replace human expertise.
Data Quality Still Matters
One phrase repeated several times during the conference was “garbage in, garbage out.”
AI systems are only as reliable as the information they receive.
Poor-quality data can lead to inaccurate recommendations, misleading field analysis, or flawed conclusions. In agriculture, local weather, soil conditions, equipment calibration, and field variability all influence how useful AI-generated insights may actually be.
Privacy Concerns With Agricultural AI
Data privacy was another major discussion topic at the conference.
Speakers encouraged attendees to avoid entering sensitive financial, operational, or proprietary farm information into public AI systems without understanding how the data may be stored or used.
Institutional or licensed AI systems may provide stronger privacy protections than free public platforms.
For agricultural businesses and landowners, protecting operational data will likely remain an important consideration as AI adoption continues growing.
The Future of AI in Agriculture
One of the strongest themes from the 2026 ISU Soil Management and Land Valuation Conference was that artificial intelligence will likely become a standard agricultural tool rather than a temporary trend.
Much like GPS guidance and precision agriculture technology became common over time, AI is expected to continue integrating into everyday farming operations.
However, conference presenters emphasized that the best outcomes will likely come from combining:
- Agricultural expertise
- Local knowledge
- Human oversight
- High-quality data
- AI-driven efficiency
rather than relying entirely on automation alone.
Final Thoughts on AI Tools in Agriculture
Artificial intelligence is already influencing agriculture through crop scouting, autonomous machinery, livestock monitoring, yield prediction, and research analysis. The technology continues advancing rapidly, and many agricultural businesses are beginning to explore how AI tools can improve operational efficiency and decision-making.
At the same time, the 2026 ISU Soil Management and Land Valuation Conference reinforced an important reality: successful agriculture still depends heavily on human experience, practical judgment, and local knowledge.
AI may help farmers and agricultural professionals process information faster and identify patterns more efficiently, but experienced people will continue making the decisions that drive successful farming and land management operations forward.
