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Fields are the atomic units of agriculture. Generating agricultural insights at an individual field level is critical to a meaningful change in the agri ecosystem.


Diversity of landscape and crops lead to very different requirements between two fields in proximity to each other


Currently, insights are available at an aggregate level, but the intervention and advisory is needed at an individual or farm level


Leveraging high resolution satellite imagery, Street View imagery, Google Maps corpus and bespoke ML models



Agriculture Landscape Understanding

Landscape understanding leverages satellite imagery and machine learning to draw boundaries between fields, the basic unit of agriculture and essential in creating meaningful insights. With field segments established, the model can determine the acreage of farm fields, forest and woodland areas, and can identify irrigation structures like farm wells and dug ponds to build tools for drought preparedness.


(Coming Soon)

Agricultural Monitoring and Event Detection

AMED data

Explore details of an individual field and review agri practice throughout different periods to better understand its needs.

  • Details crop type, field size, distance to water, distance to road. Future capabilities to include distance to cold storage and distance to market.
  • Track agri practices over 3 years.
  • Explore specific activities like sowing, harvest, or tillage for a specific field.

Our Partners

Partnerships with state governments, academic institutions and agritech enterprises