The flagship platform of Indigo’s GeoInnovation team, Indigo Atlas delivers a living map of the world’s food system. Atlas Insights derives unparalleled, nuanced detail from this platform. Capable of characterizing localized soil conditions, drawing field boundaries to meter-level accuracy, and discerning subtle differences in crop health across a region, Atlas’s proprietary algorithms generate a range of high-resolution, real-time, and actionable outputs. Atlas information enables growers to make more informed planting, harvest, and marketing decisions. It also grants buyers and consumers a greater degree of control and choice over their supply chain.

How we generate insights

A realtime plant health index optimized for agriculture

For decades, vegetation indices have seen many use cases across life sciences, but none has been tailored to the unique needs of agriculture. To track the dynamic variables affecting crop health, Indigo has created the Crop Health Index (CHI). We follow multiple crops across several geographies to better understand our global food system — and help you make better decisions in realtime.

A living map of the world's food supply

As part of its living map of the world's food system, Indigo  forecasts crop yield and production around the globe with a satellite imaging and machine learning platform called Atlas. Atlas’ yield prediction capabilities have outperformed the USDA's in recent years. With the application of machine learning and artificial intelligence to satellite imagery, Indigo Atlas generates global crop health and performance daily. Indigo stakeholders, including growers and partners, leverage this data for myriad objectives throughout the season.

If you take a look at the image shown here of the Mississippi River, you can see what is called an Enhanced Vegetation Index (EVI) where the image is comprised of multiple data layers stacked over time. The input images were acquired throughout the 2018 growing season by the European Space Agency 's twin Sentinel -2 satellites, which image the earth every four days. These multi-temporal image composites are the raw ingredients used in Indigo 's field boundary identification and crop type mapping efforts.

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