The most interesting AI opportunity today is hiding in farming (sponsored)

Small farms generate a constant stream of highly valuable data on pest outbreaks, treatment outcomes, climate effects and soil responses. But after the season ends, insights are lost. But it doesn’t have to be this way, says AI agricultural venture Greenda.

Spain is Europe’s leading citrus producer, supplying oranges, mandarins, lemons, and other fruits to markets across the continent. But the local agricultural cooperatives that are in charge of growing your favourite citrus have a hundred responsibilities: they need to maintain consistent crop outcomes across thousands of small, geographically dispersed farms, provide farmers agronomic support and ensure regulatory compliance – all at the same time.

In practice, that means that a small number of agronomists that work for these coops operate under increasing pressure. They are expected to monitor, advise, and document decisions across hundreds of plots at the same time – and that model is starting to strain.

Recognising this squeeze, the Greenda team spoke to hundreds of farmers and visited both coops and plots. Here is what they heard, again and again.

Info is scattered and trends are invisible

First, field coverage is structurally limited. Agronomists simply cannot physically visit all farms at the frequency that pest and disease dynamics require to act preventatively. Monitoring becomes reactive rather than continuous, often based on sample visits rather than full visibility.

Second, administrative and compliance burdens are on the rise. With upcoming regulatory shifts such as the EU’s digital farm notebook requirements, agronomists are spending more time documenting decisions and treatments – and less time in the field where those decisions originate.

Third, coordination is fragmented. Farmers often report issues through WhatsApp, phone calls, or other informal channels. Information is scattered across conversations, spreadsheets, and memory. That makes it difficult to detect patterns across regions or to synchronise interventions between neighbouring plots.

Agronomists and coop managers describe a recurring pattern: valuable field information exists, but it is not structured in a way that can be reused across seasons or aggregated across farms. Over time, that has led to predictable inefficiencies practically everywhere: overuse of pesticides, inconsistent treatment timing, and preventable crop losses that only become visible at harvest.

In other words, there’s plenty of scattered signals and data – but systemised intelligence is few and far between. And it’s a problem that is inherently solvable.

It’s time for a coop-wide intake and triage system for crop issues

Munich-based startup Greenda has innovated a workflow and data layer designed to sit between farmers and agronomic expertise that solves this crucial problem for coops (and by extension, for our entire food supply system). Here’s how it works:

Farmers can submit simple photos of a crop issue through a Whatsapp-like app. The system helps structure the incoming information using AI-assisted analysis, while a certified agronomist reviews each case before any recommendation is returned. That’s an excellent feature – but what happens on the cooperative side is what makes it interesting.

Instead of fragmented incoming messages, coops receive structured, traceable cases that can be prioritised, compared, and tracked over time. Agronomists can see where issues are emerging, how severe they are, and where intervention is most urgent. This transforms the workflow from individual troubleshooting into coordinated field management.

As Chadi Nemer, CEO of Greenda, puts it: “Greenda’s ambition is to be the platform that defines what good looks like for coops. Imagine territory-level intelligence that makes it possible to manage 400 plots with the same quality of attention you’d give 40. Zone-level pest maps, early outbreak signals, automated documentation – the kind of systemic visibility that today requires either a lot of staff or a lot of luck.”

Extending, not replacing, cooperative expertise

A recurring concern in AgriTech is whether AI can (or should) replace human agronomic judgment. Greenda’s view? That the constraint inside cooperatives is not a lack of expertise but a lack of scalable presence. Agronomists simply cannot be everywhere at once, and they cannot systematically process every signal they receive in real time.

Greenda’s approach is therefore explicitly human-in-the-loop. Agronomists remain central to decision-making. The system is designed to extend their reach, not bypass their expertise – reducing back-and-forth communication, standardising intake, and ensuring that decisions are documented and reusable.

Creating institutional knowledge in agriculture

Today, much of the knowledge inside cooperatives exists in WhatsApp threads, notebooks, or the memory of individual agronomists. The data that arises with every crop issue, treatment decision, and outcome, as well as the learnings based on years of field context, simply *poof* disappears when farmers retire or staff changes roles.

Greenda’s thesis is simple: cooperatives sit at the centre of vast agricultural networks. They already receive signals daily from thousands of farms. What they lack is a unified layer that can turn those signals into coordinated action and long-term learning.

Today, the opportunity for this kind of solution is vast: Europe’s 9 million farms are represented by more than 40,000 agricultural cooperatives – making the cooperative the most efficient place to turn millions of day-to-day field signals into structured, reusable intelligence.

But Greenda’s horizon is far longer than improving one season’s workflow. Over multiple seasons, the same coop-level data layer becomes the basis of a predictive pest management platform: early outbreak signals, territory-level risk maps, and evidence on what interventions actually work under specific conditions.

That kind of verified, real-world insight is exactly what the next set of beneficiaries will pay for – governments planning food security and compliance policy, insurers pricing agricultural risk, and agribusinesses managing supply resilience – because it converts fragmented farm activity into decision-grade intelligence at scale.