Three Roles of AI in Farming: Insights from Top Agrar and RankmyAI
Written by David Kakanis
Artificial intelligence has become increasingly common in agriculture, especially in crop production. You hear a lot about technological breakthroughs, but the reality on actual farms is more complicated. Some systems that rely on artificial intelligence are already being used every day, while others are still being tested.
RankmyAI recently collaborated with Top Agrar, a German agricultural trade journal, on an article about the use of artificial intelligence in agriculture. The article looks at how AI technologies are used in agriculture under real farming conditions, particularly in crop production and weed control.
This blog post provides an English summary of the main points from that article.
Three Different Ways Artificial Intelligence Shows Up in Farming
When you look at agricultural applications, Top Agrar found that artificial intelligence typically plays three distinct roles. These roles differ in how closely the technology interacts with actual field operations, how much data the system needs to function, and how much decision-making power gets handed over to the machine.
The first role is when artificial intelligence directly controls mechanical actions in the field. Think of robots, tools, or actuators that physically do work like hoeing between rows, pulling weeds, or harvesting crops. The intelligence guides the machine's physical movements.
The second role involves making decisions about chemical interventions. Here, the artificial intelligence determines exactly where crop protection products should be applied and how much should be used in each spot. This enables targeted spraying and what farmers call variable-rate application, where different parts of the field get different treatment levels.
The third role is supporting strategic decisions at a higher level. The artificial intelligence analyzes satellite imagery, sensor data, and farm management records to help with planning, monitoring, and decision-making. This is less about immediate field actions and more about the bigger picture.
Each of these roles puts different demands on the system. They need different levels of data quality, network connectivity, and agronomic consistency. They also work under different business models. Weed control robotics shows these differences particularly clearly.
Looking at Weed Control as a Real Example
Weed control turns out to be a great way to see how unevenly agricultural artificial intelligence is being adopted. Rising labor costs and new restrictions on chemical herbicides are pushing farmers to look for automated solutions. But the autonomous weeding systems available today differ dramatically. Some rely on rigid precision automation, while others use fully data-driven approaches that make decisions about individual plants.
Let's look at three specific approaches currently being used in practice.
FarmDroid FD20 uses precise GPS to map seed positions during planting, then returns to hoe around those exact spots. It's highly accurate but completely rigid—there's no plant recognition, so it can't adapt if conditions change.
Ekobot adds cameras and trained models to recognize crops from weeds in real time. The vision is intelligent, but weed control stays mechanical. It adapts to what's actually growing but needs stable conditions and crop-specific training.
Odd.Bot Maverick goes furthest, using artificial intelligence both to recognize plants and decide which weeds to remove. Robotic grippers selectively pull individual weeds, even within crop rows. This saves considerable labor but demands extensive training data and ongoing maintenance.
When you put these three systems side by side, you can see that saying a system "uses artificial intelligence" doesn't tell you much. The details of how and where that intelligence is applied make all the difference.
Final Thoughts
Artificial intelligence in agriculture isn't hype, but it's not a universal solution either. It's a collection of tools that can deliver substantial value under the right conditions. Those conditions include appropriate agronomic practices, adequate technical infrastructure, and supportive organizational structures.
The examples from weed control show that adoption is definitely advancing. But it's happening selectively and unevenly, not as a uniform wave of technology sweeping across all farms simultaneously. Understanding how artificial intelligence is being used, rather than simply knowing that it's being used somewhere, is essential for making informed decisions about investment, deployment, and policy.
For farmers considering these technologies, the key is matching the right type of system to your specific situation. What works brilliantly on one farm might struggle on another, even if the crops and climate are similar. The infrastructure, data availability, labor situation, and long-term support all matter just as much as the technology itself.
