Belgian and European farming faces a hard equation: produce more, with fewer hands and fewer inputs, on soils that must be spared. The FAO estimates that 20 to 40% of the world's crops are lost every year to pests and diseases, while the European Union lost 5.3 million farms in fifteen years. AI in the field, so-called precision agriculture, promises to close that gap. Here is what it already does, what it returns, and its limits.
Two pressures collide. On one side, demand: the FAO estimated that around 70% more food would need to be produced by 2050 to feed a population above 9 billion. On the other, the human resource is thinning: between 2005 and 2020 the European Union lost 5.3 million farms (a 37% drop) and its agricultural workforce fell by 36% (Eurostat). More must be produced with fewer people, and automation stops being a luxury.
The market follows. According to MarketsandMarkets, precision agriculture is worth 11.4 billion dollars in 2025 and should reach 21.5 billion by 2032, growing 9.5% a year. The motivation is also defensive: plant diseases cost the global economy roughly 220 billion dollars a year, and invasive insects nearly 70 billion (FAO). Every point of loss avoided in the field is a point of margin and an input saved.
In Flanders, the public research institute ILVO makes precision agriculture and data a central pillar: its data-sharing platform DjustConnect was named « best European Data Space », and the institute takes part in several projects on smart weed control using robotics and AI. In Wallonia, the Digital Agency promotes smart farming, notably at the Libramont fair. The Belgian ecosystem is not watching the shift, it is building it.
Precision agriculture rests on a simple loop: observe, decide, act, measure. AI mostly steps into the « decide » stage: it turns a stream of heterogeneous data (images, sensors, weather, history) into intervention maps and actionable alerts. The building blocks are now mature and, for the most part, accessible.
Vendor promises are plentiful; solid evidence less so. A meta-analysis published in 2025 in Sustainability integrated 85 studies and 1,472 farms worldwide. Average result: adopting precision technologies raises return on investment by 22.3% and net profit by 18.5%, improves nitrogen use efficiency by 15.1%, and cuts pesticides by 12.8% and greenhouse-gas emissions by 9.4%.
The same study adds a decisive caveat: these gains are highly context-dependent. They are clearest on large grain farms, with variable rate and auto-guidance; they are weaker and more variable on small operations and in developing countries, where the entry cost remains a barrier. In other words, the question is not « does it work? » but « under what conditions, and for whom? ».
Do not deploy everything, everywhere, at once. Pick a test plot and a measurable goal (cut nitrogen, spot a disease earlier, save water), document before and after, then scale what proved its worth. Precision is won through iteration, not a technological big bang.
List your plots, soil variability and yield history. A yield map over several seasons already reveals where precision intervention will pay off most.
Start with the useful minimum: satellite or drone imagery, a few sensors, GPS-RTK guidance. No need to buy the whole ecosystem before validating a use case.
Variable-rate maps, disease alerts, irrigation recommendations. AI proposes, the agronomist decides: keep a human in the loop for high-impact calls.
Compare costs, yields and inputs before and after, and clarify who owns and shares your data. Farm data is an asset: treat it like one, as with a controlled data strategy.
Three caveats deserve attention. The first is the entry cost: hardware, subscriptions and skills weigh heavily on a small farm, which explains the variability of results. The second is the rebound effect (Jevons paradox): the savings achieved can encourage more intensive cultivation, erasing part of the environmental benefit. The third is data ownership: who owns the maps, the yields, the images? Equipment-maker contracts must be read carefully, and initiatives like DjustConnect aim precisely to keep the farmer in control of their data.
The regulatory framework follows. The EU AI Act imposes transparency and robustness as systems gain autonomy, and the GDPR applies as soon as personal data (employees, customers, location) passes through a model. The Common Agricultural Policy also ties part of its support to more sustainable practices, which precision can help document. Done well, precision agriculture therefore serves both margin and compliance.
It is steering each plot, or even each plant, from data (satellite, drone, sensors, yield history) to apply only the right dose of water, fertilizer and treatment, in the right place at the right time. AI turns that data into decisions and intervention maps.
Not necessarily, but scale helps. The global meta-analysis shows clearer and more stable gains on large grain farms, weaker and more variable on small operations. For a Belgian farm, the sensible path is to start with a high-return building block (remote sensing, nitrogen variable rate) on a test plot, then expand.
The farmer who produces it, in principle, but equipment-maker and platform contracts can blur ownership and use. In Flanders, the DjustConnect platform was designed so the farmer keeps control of how their data is shared. Read the portability and reuse clauses before committing.
With one plot and a precise goal: cut nitrogen, spot a disease earlier, save water. Measure before and after (costs, yield, inputs), check data ownership, and align with the GDPR as soon as personal data is involved. Then scale what proved its value.
Molderez Consult helps Belgian agricultural and agri-food players frame their AI use cases, pick the right building blocks (remote sensing, sensors, vision, variable rate), secure data ownership and stay compliant with the EU AI Act and GDPR, from test plot to rollout.
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