AGR.01
Crop & Drone Vision
Satellite, drone and phone imagery turned into ranked scouting lists for stress, disease and weeds, validated against local ground truth; agronomists confirm every cause.
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Agriculture AI,
rooted in the field.
Field data first. The agronomist decides. Crop and drone vision, agronomy advisory agents and CAP paperwork for agtech companies, cooperatives and advisers. Built on real field data from your region, with the agronomist and the farmer accountable for every decision.
Last reviewed 30 October 2026 · Engineering guidance, not legal or regulatory advice.
In context
AI in agriculture means turning field imagery, sensor data, local trial results and regulatory rules into earlier warnings, better-informed advice and less paperwork. It works when models are checked against ground truth from the region they serve, and when an agronomist or the farmer makes every decision about what goes on a crop.
Where it breaks today
01
Imagery without answers
Drone and satellite images arrive as colourful maps. Turning an NDVI dip into “this is water stress, that is a disease patch, scout here first” still takes an agronomist walking the field.
02
Advice does not scale
A good adviser covers a limited number of farms. Generic chatbots answer confidently about products not registered in your country or rates that are off-label.
03
Paperwork grows every season
CAP aid applications, eco-scheme evidence, conditionality records and digital field notebooks add up to days of forms that duplicate data the farm already holds.
OUR APPROACH
Local ground truth beats global models
A disease model trained on wheat in one climate can mislead in another; a product rate valid in one country can be illegal across the border. So we build agricultural AI around local evidence: imagery labelled by your agronomists in your region, your own trial results, the national register of authorised plant protection products and the farm’s own records. Generic models are a starting point, never the answer.
And the person stays in the loop where the law and the crop both expect one. A vision model points to where to scout; it does not diagnose. An advisory agent drafts a recommendation with its sources; a qualified adviser signs it where your country requires that. A CAP application is prepared from the farm’s data; the farmer declares and submits it.
HOW IT DEPLOYS
PHASE 01
One crop, one region
We start narrow, with a crop and area where your agronomists can label ground truth.
PHASE 02
Connect the farm systems
Imagery, machinery data and farm management platforms such as John Deere Operations Center, Climate FieldView or your own FMIS.
PHASE 03
Validate in season
Predictions checked against scouting reports through a real season before anyone relies on them.
What we build
01
Crop & Drone Vision
Drone, satellite and handheld imagery turned into scouting priorities: stress, disease, weed and stand-count signals, ranked by where an agronomist should look first.
SEE CROP VISION →02
Agronomy Advisory Agents
Agents that answer growers from local trial data, authorised product labels and the farm’s own history, citing each source and handing decisions to an adviser.
SEE ADVISORY AGENTS →03
CAP Subsidy & Compliance Paperwork
Aid applications, eco-scheme evidence and digital field records prepared from data the farm already holds, ready for the farmer to check and submit.
SEE CAP PAPERWORK →04
Yield & Demand Forecasting
Yield and supply forecasts for cooperatives and buyers from field, weather and historical data, with uncertainty shown rather than hidden.
SEE FORECASTING →Agriculture use cases
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AGR.02
Agronomy Advisory Agents
Grower questions answered from local trials, authorised product labels and farm records, every source cited, with treatment recommendations drafted for an adviser to sign.
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AGR.03
CAP Subsidy & Compliance Paperwork
CAP aid applications, eco-scheme evidence and digital field records prepared from existing farm data and checked against the rules; the farmer verifies and submits.
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Questions
Questions about Agriculture & AgTech.
How is AI used in agriculture?
Mostly in four ways: computer vision on drone, satellite and camera imagery to spot stress, disease and weeds; predictive models for yield, weather risk and input timing; advisory agents that answer growers from agronomic data; and automation of records and subsidy paperwork. Machinery makers also build vision into sprayers and harvesters. Each works best when tuned to local crops and conditions.
How will AI impact farming?
It shifts time from routine observation and paperwork to decisions. Imagery tells an agronomist where to walk, records fill themselves from machinery data, and advice reaches more growers. It does not remove the need for local knowledge: models trained elsewhere can mislead, and decisions about inputs, harvest and compliance stay with the farmer and adviser.
What are the disadvantages of AI in agriculture?
Models trained in one region can perform poorly in another, connectivity in rural areas limits real-time use, data ownership between farmers, cooperatives and platforms is often unclear, and confident generic advice can be wrong or off-label. We address these by validating on local ground truth, designing for offline use where needed and agreeing data ownership in writing.
Can AI replace agronomists?
No. AI extends an agronomist’s reach by prioritising which fields to scout and drafting advice from evidence, but diagnosis in the field, judgement about local conditions and, in many countries, the legal signature on a plant-protection recommendation remain human. The aim is one adviser serving more growers well, not fewer advisers.
Who owns the farm data used by AI?
It should be agreed in writing before any data moves. The EU Code of Conduct on agricultural data sharing sets out principles in which the data originator, usually the farmer, controls access. We build systems where the farmer’s or cooperative’s data stays on infrastructure they control and is not used to train anyone else’s model.
