Cropping Up in Court: The Coming Legal Battles Over Ag AI Ownership

4 Jun

It was probably inevitable. A field fails, a farmer stands there, arms crossed, trying to work out how everything went so wrong when the data said it would go so right. The AI-powered irrigation system had adjusted itself based on last month’s rainfall and a soil moisture reading that didn’t match what his boots had told him. The yield dropped, the season was lost, and now he’s preparing to take legal action, although who he is supposed to sue isn’t exactly obvious.

This is the grey zone we’re entering, quietly and quickly, as farms become more reliant on systems that make decisions, not just suggestions or reminders, but actual autonomous choices that are executed with little or no human oversight. When those decisions go wrong, the question of accountability isn’t just uncomfortable, it’s legally unresolved.

What began as forecasting tools and dashboards has become something more active. These systems now schedule irrigation, adjust nutrient levels, apply pesticides, direct robotic equipment, and make real-time choices based on predictive models that were trained using data the farmer may never have seen and possibly wouldn’t trust if they had. The result is a situation where the person on the land isn’t always the one calling the shots, even though they’re still the one held responsible when something goes wrong.

Legal structures have not caught up. In a recent US case, Jones v. W + M Automation, a farmer sued after an autonomous system caused damage to crops, but the court found the manufacturer wasn’t liable because the components weren’t defective. In other words, the parts worked as designed, even if the outcome was disastrous. This opens the door to a new kind of risk, one where systems are too complex to fully understand and too fragmented to easily blame.

There are also broader signs that regulators are starting to take notice. The US Federal Trade Commission launched an enforcement sweep in 2024, called Operation AI Comply, targeting several companies, including those in agriculture, for overstating what their AI systems could actually do. This matters because it shifts the focus onto the truthfulness of claims made about AI in commercial farming and puts responsibility back on the companies who develop and market these tools. It’s a start, but it’s far from a finished framework.

One of the most difficult issues is data. Farmers generate massive quantities of it through planting, harvesting, monitoring, and even walking the field, yet ownership of that data is rarely straightforward. In many cases, the terms of use on a software platform will give the company broad rights to store, analyse, and retain that data, while the farmer is given limited access, little portability, and no say in how that information is used to train models or generate future insights.

It becomes especially tricky when systems begin making decisions based on that data. If a platform uses a grower’s history to generate a fertiliser plan or a yield forecast, who owns the result? Can it be shared? Is it protected? If a farmer cancels their contract, do they lose all rights to those insights? This is already happening, quietly, in background clauses and standard contracts, and the implications are becoming harder to ignore.

The EU has proposed an AI Liability Directive that aims to make it easier for people to bring claims when AI systems cause harm. Legal analysts have raised concerns, though, that the language is too vague to offer much protection in complex, technical fields like agriculture. Meanwhile, the UK, operating post-Brexit, has yet to publish a proper roadmap for regulating AI in food production. DEFRA has made occasional references to innovation in digital farming but little in the way of policy guidance.

In the United States, the picture is even less stable. A 2024 Supreme Court ruling in Loper Bright Enterprises v. Raimondo removed the long-standing Chevron deference, which had previously required courts to respect the judgment of expert agencies in interpreting unclear rules. Without that, more disputes will likely end up in courtrooms, with outcomes that are inconsistent and case-dependent, rather than guided by regulatory clarity.

There are also concerns about the limits of legal accountability. AI systems are being treated as tools, but they are behaving more like agents. If an AI makes a decision that causes loss, should responsibility fall on the farmer who used it, the company that built it, or the data scientist who trained it on a flawed dataset? Current law tends to hold the user responsible, even when they had no practical way to review or override the model’s logic. That isn’t sustainable, and it’s already putting off smaller growers from investing in systems that might otherwise help them.

Some legal scholars have suggested that highly autonomous AI might eventually need some form of legal status. Not full personhood, but a designation that allows for accountability in cases where fault is too diffuse to assign clearly. This sounds theoretical, but it’s beginning to surface in discussions around high-risk automation. The idea is not to let companies off the hook, but to create frameworks for when machines act with some degree of independence that makes traditional liability models feel outdated.

All of this leaves the farmer in a difficult position. Expected to keep up with new systems, to produce more with less, to trust platforms that are rarely transparent, and to shoulder the burden when those systems misfire. What’s needed is not a rejection of AI in agriculture, but a clearer structure around it — contracts that are fair and readable, systems that offer explanations and audit trails, policies that define where responsibility lies.

Farmers need to be asking harder questions before adopting new tools. Where does the data go? Who owns it? What happens when something fails? Can decisions be overridden? Is there a right to appeal? These are not unreasonable demands. They are the foundation of trust in a system that is still very much in flux.

Governments also have a role to play. This technology cannot remain unregulated for much longer without creating widespread legal risk and disincentivising adoption altogether. Lawmakers need to work with growers, researchers, and tech developers to create rules that make sense in the real world, not just on a policy brief.

Lastly, there must be room for proper public discussion. Not just at conferences and expos, but in farming communities, in local co-ops, in the places where these technologies are being used day in and day out. Because if farmers are expected to rely on AI for decisions as critical as planting, harvesting, and treatment, they should be given every opportunity to understand, challenge, and shape how those systems are designed.

Farming has never been predictable. But the unpredictability of weather or markets is something most growers know how to live with. The unpredictability of contracts, algorithms, and liability? That’s a different kind of risk entirely. It deserves our full attention before the next lawsuit makes it impossible to ignore.

Cropping Up in Court: The Coming Legal Battles Over Ag AI Ownership

Further Reading and Resources

Data Wars: How Private Equity is Buying Up the Agricultural Gene Pool

The Digital Farmhand: AI’s Role in Sustainable Crop Cultivation