EOSDA Crop Monitoring

Agriculture · Weather & Environment

EOSDA Crop Monitoring is the flagship precision agriculture platform from EOS Data Analytics, a global Earth observation and geospatial analytics company. The platform provides satellite-based crop monitoring, NDVI field analytics, weather integration, field scouting tools, and yield prediction capabilities delivered through a REST API and a web application interface. EOSDA Crop Monitoring is designed for agritech developers, precision farming service providers, and agricultural enterprises that need to monitor crop health across portfolios of farm fields using satellite imagery as the primary data source.

paid

API Key

Configurable based on plan

✗ Not Available

Best For

Precision farming and advanced crop monitoring

Pricing

Global precision agriculture platform with strong African coverage. EOS Data Analytics (EOSDA) provides enterprise satellite analytics. Nigerian farmland is well-covered. Primarily used by agri-insurers, lenders, and large-scale farm operators.

Key Highlights

  • NDVI and EVI vegetation indices derived from Sentinel-2 and Landsat satellite imagery — updated every 5–10 days per field
  • Soil moisture mapping combines satellite data and weather models to show soil water content across farm fields
  • Crop type classification identifies what is planted on each field — useful for insurance, lending, and government planning
  • Field boundary mapping tools allow farmers and agronomists to define and monitor individual farm plots via the API
  • African satellite coverage is excellent — Nigerian farmland across all agro-ecological zones is well-served

Required Access Documents

  • API KeyRequired
    Sign up for a developer account to obtain an API key/credentials.

Use Cases

  • Precision Crop Monitoring:Monitor crop health with high-resolution satellite data and vegetation indices.
  • Irrigation Planning:Optimize irrigation schedules using soil moisture and weather data.
  • Yield Forecasting:Predict crop yields using satellite data and machine learning models.
  • Disease Management:Detect crop stress early through satellite monitoring.
  • Field Analysis:Analyze field variability to optimize inputs and manage zones.

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