Farmers, growers and livestock keepers process enormous amounts of data every day: soil measurements, satellite images, weather forecasts, sensor data from cows and drones flying over fields. In the agricultural sector, AI is no longer a gadget; it is the translation layer that turns raw field data into concrete action. Yet there are dozens of tools, and it is not always clear what you need when. This guide helps you get started.
Where AI concretely helps in this field
The agricultural sector has four core tasks where AI makes a measurable difference:
Crop monitoring and disease detection
Satellite images with NDVI and NDWI values show, per field, how healthy the crop is. Detection of fungi, weeds or insect damage at leaf level can be done via drones, weeks earlier than visible to the naked eye. That gives you time to intervene in a targeted way instead of treating an entire field preventively.
Precision fertilising and irrigation
Soil moisture monitoring combined with weather data calculates exactly when and how much water or fertiliser a field needs. Variable rate application (VRA) maps steer machines so that each part of the field receives the right amount, not the average for the whole plot.
Monitoring animal health
Cameras and sensor boluses measure rumination time, movement, temperature and body condition for each individual animal. AI detects deviations days before clinical symptoms become visible, so you can call in a vet before the animal is actually ill.
Farm planning and administration
Digital twins of the farm enable yield forecasts based on historical data and current field information. Linking to field records, plant protection logs and manure bookkeeping reduces the administrative burden that comes with RVO obligations.
What to watch out for: data, GDPR and responsibility for use
Agricultural data is commercially sensitive. Field yields, soil scan results and animal health history are valuable trade secrets. Check three things with every platform:
- Is field data used for model training? Large platforms sometimes use anonymised farm data to improve their AI. Read the data processing agreement (or ask for it).
- Is the data stored outside the EU? American and Australian platforms often store data in the US. That is not necessarily prohibited, but it requires adequate safeguards (SCCs). The EU Data Act also sets specific requirements for agri-data shared within supply chains.
- Who is responsible for spray advice? AI crop protection recommendations are supportive. The spraying licence, the obligation to keep usage records and legal liability remain with the entrepreneur. Always document where you followed an AI recommendation and why.
Animal data in livestock farming can contain personal data if sensors are attached to identifiable animals linked to an owner. In that case, the GDPR applies in full.
3 concrete starting scenarios with tools from our index
Scenario 1: arable farmer wants to introduce precision fertilising
You have 80 hectares of arable land and want to apply fertiliser variably based on current crop imagery. Start with OneSoil, the free basic version already gives you NDVI maps per field using Sentinel-2 satellite data and lets you export variable rate maps. A free start gives you insight straight away without any investment.
If you also want to include soil moisture and automated irrigation recommendations, look at CropX. CropX combines soil moisture sensors (a one-off purchase of about €1,000 per sensor) with a satellite layer and calculates daily irrigation recommendations per zone. Pricing is on request based on area, budget for multiple sensors on larger fields.
For those who also want to outsource leaf-level scouting to a drone: Taranis offers seasonal packages for aerial imaging with AI detection of weeds, fungi and pests. Costs here too are on request, depending on the number of hectares and flights per season.
You can combine internal planning and logging with AGRIVI, which brings field records, input management and traceability together in one platform, handy for RVO administration. Pricing on request.
Scenario 2: dairy farmer wants to monitor animal health better
You have 120 dairy cows and want to flag sick animals earlier. There are two good starting points:
Nedap Livestock Management is a Dutch system with a strong reputation. Sensors monitor oestrus, health and milk yield per cow; the SmartSight camera detects lameness through image analysis. Hardware plus a subscription per cow, price on request, but Nedap works with accredited dealers in the Netherlands and offers free trials.
smaXtec takes a different approach: a sensor bolus swallowed by the animal continuously measures body temperature, rumination and water intake from the inside. The AI detects illness up to four days before clinical symptoms appear. Market indication: bolus approx. $89 one-off, subscription approx. $2.49 per cow per month. Request exact Dutch prices from the dealer.
Already have a Lely robot system in place? Then Lely Horizon is the logical extension: the platform connects to your milking robots, monitors health models and predicts, among other things, ketosis. Prices are part of the Lely package.
Scenario 3: mixed farm wants to improve oversight and planning
You have arable farming and livestock but lack oversight. John Deere Operations Center offers a free basic account that brings machine data, field information and crop plans together in one place. It works best if you have John Deere machines (direct integration), but it also accepts data from other brands. Advanced features such as See & Spray (targeted spraying technology) are extra and available on request.
xarvio FIELD MANAGER from BASF complements this with field-specific sowing and fertilisation advice based on satellite and weather data, including weed and disease management. The app is free to download; the annual subscription for professional use is available on request.
Common mistakes
Buying too many sensors too quickly. Soil moisture sensors, GPS stations, cameras, the costs add up fast. Start with one use case (e.g. crop monitoring via satellite, the cheapest option) and only scale up once you know which data you actually use.
Storing data in the cloud without a data processing agreement. Especially with foreign platforms: always request a DPA (data processing agreement) before uploading your business data. Many platforms supply one as standard, but you sometimes have to ask for it explicitly.
Following AI spraying advice without question. AI tools advise based on image recognition and historical data, but they do not fully know your local situation. Always combine it with your own expertise and consult an accredited crop protection adviser if in doubt.
No backup of field data. If years of plot and yield history exist only within the platform, you depend on that supplier's continuity. Export your data regularly to your own storage.
Cost overview: what can you expect?
Most agri-AI tools use a price-on-request model, because costs depend heavily on farm size (hectares, number of animals). An indicative overview based on our data:
| Category | Entry price | Basis |
|---|---|---|
| Satellite monitoring (crops) | Free (OneSoil basic, Farmonaut from $30/mth) | By area |
| Soil moisture sensors | ~€1,000 per sensor (CropX) | One-off hardware + subscription |
| Farm management software (basic) | Free (John Deere Operations Center) | Advanced features on request |
| Animal health system | ~$2.49 per cow/month (smaXtec indication) | Hardware + subscription |
| Drone scanning (crops) | Seasonal packages on request | Per hectare and flight |
For an arable farm of 50 hectares starting with satellite monitoring, the threshold is low: start for free via OneSoil or Farmonaut ($30/mth) and only scale up once the added value is proven. Livestock farmers investing in health monitoring should expect hardware plus a subscription per animal, with 100 cows, $2.49 per cow/month ($249/mth) is quickly recovered if it prevents one ICU treatment per year.
Get started
The agricultural sector now has thirteen specialised AI tools in our index, ranging from free satellite dashboards to professional livestock health systems. Start with a free option, measure the effect on one task, then decide whether to expand. Want to compare more? See the full overview for the agricultural sector.
