Earth observation for agriculture means applying AI to satellite imagery so farmers and agri-businesses can make better decisions about what is happening in their fields right now. Pest risk prediction, crop growth monitoring, variable-rate fertilisation, irrigation planning and harvest timing are the use cases with the strongest evidence. The satellite data is free (Copernicus Sentinel-2 delivers a new image every 5 days). AI is what turns those images into field-level recommendations. We combine AI engineering depth with EU funding access through the FIERCE programme – up to €50,000 per project.
Pest risk prediction. Satellites reveal stress signatures in crops before infestations become visible to the human eye. AI models trained on multispectral vegetation indices flag fields at risk days or weeks before the damage spreads, giving farmers time to intervene with targeted treatment rather than blanket spraying.
Crop growth monitoring. Data on leaf area, chlorophyll content and water status across every field, updated every 5 days. AI aggregates this into growth stage tracking and yield forecasts that help cooperatives and traders plan logistics and pricing.
Variable-rate fertilisation. Instead of applying the same rate across an entire field, AI generates variable-rate maps from satellite vegetation indices. Fertiliser or seeds go only where needed, reducing input cost and environmental runoff.
Irrigation planning. Soil moisture estimates from radar satellites (Sentinel-1) combined with evapotranspiration models let farmers align water usage with actual plant needs, not a fixed schedule.
Harvest timing. Temperature accumulation, growth stage tracking and weather forecasts combined by AI to predict optimal harvest windows – reducing crop loss and improving quality.
Crop monitoring
Precision inputs
Water management
Input costs down
fertiliser, pesticide and water applied only where and when needed
Up to €50,000 EU funding
through the FIERCE programme for SMEs and startups integrating space tech into green solutions
Field-level decisions
AI turns satellite passes into per-field recommendations your agronomist can act on
1. Define the use case
2. Build the analysis pipeline
3. Deliver and operate
We are an AI agency with a decade of impact-tech experience and over 160 AI projects since 2019. We combine the engineering depth that production AI needs with the EU funding access and Earth-observation network we have built through FIERCE and CASSINI. The 44 use cases on our Space Tech page include 6 agriculture-specific applications already validated by EU FIERCE service providers.
Satellite resolution has limits. Sentinel-2 delivers 10m pixels – enough for field-level analysis, not for counting individual plants. For sub-metre resolution you need commercial providers or drones.
Cloud cover blocks optical satellites. In cloudy regions, radar satellites (Sentinel-1) provide an alternative, but the data is harder to interpret. AI helps, but some use cases need cloud-free optical imagery.
Models need local calibration. A pest-risk model trained on Central European wheat will not transfer directly to Mediterranean vineyards. Ground truth from your region is part of the setup cost.
Satellite data is not a replacement for agronomic expertise. It is a tool that gives your agronomist better information, faster. The decision still needs a human.
What crops does this work for?
How often do I get new data?
Can I integrate this with my farm management software?
How does FIERCE funding work?
Tell us about your crop, your region and the decision you want to improve. We will reply with a proposed approach and a date, usually within a working day.
Simon Stegemann
Co-Founder and CEO
