EOSDA Agriculture API
Agriculture · Satellite Imagery
The EOSDA Agriculture API is EOS Data Analytics' precision agriculture platform API that delivers satellite-derived crop monitoring, NDVI field analytics, vegetation stress detection, and integrated weather intelligence to agritech developers and farm management systems. EOS Data Analytics is a global Earth observation company that has built a specialized agriculture product using satellite imagery from Sentinel, Landsat, and commercial satellite constellations to provide field-level crop health insights.
EOSDA Agriculture API Highlights
EOSDA Agriculture API is verified for developer integrations in Nigeria and across African markets, providing documented endpoints, sandbox testing, and developer support.
API Key
Varies by plan; free tier limited fields and calls
Nigeria
Ghana
Kenya
AWSEOSDA Agriculture API is best for:
Precision farming platforms needing satellite-based crop monitoring, NDVI analytics, and field scouting tools
- Free tier with limited fields
- paid plans from $29/month with higher field counts and API calls
EOSDA Agriculture API Core Features & Capabilities
Required Access Documents for EOSDA Agriculture API
- API KeyRequiredAPI key from EOS developer account
- Field BoundariesRequiredGeoJSON polygon defining farm field boundaries for monitoring setup
Different EOSDA Agriculture API use cases
- Crop Health Monitoring for Nigerian Farms:Nigerian agritech platforms can use the EOSDA Agriculture API to register farm fields and receive regular NDVI-based crop health updates, enabling farmers to identify stressed areas before visible signs appear on the ground.
- Remote Farm Scouting:Extension workers and agricultural supervisors in Nigeria managing multiple farms can use satellite-based field monitoring to prioritize in-person site visits to fields showing anomalous vegetation indices.
- Growing Season Analytics:Nigerian agricultural platforms tracking seasonal crop performance can access historical NDVI data to compare current-season field health against historical averages and detect underperforming fields early enough for intervention.