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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.

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

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

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

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Agriculture Landscape Understanding

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Landscape understanding leverages satellite imagery and machine learning to draw agricultural boundaries of 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. Similarly other landscape elements like water bodies and vegetation can be identified, which can help with drought contingency planning.

Read our Research paper

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Agricultural Monitoring and Event Detection

AMED data

For the identified agricultural fields, via AMED API we provide historical and current in-season crop monitoring. AMED builds on ALU, and can be queried the same way ALU is. Predicted data is organized at field level in chronologically ordered crop seasons containing the predicted crop labels.

Read our Research paper

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Our Partners

Partnerships with state governments, academic institutions and agritech enterprises