Dimitra Technical Series: Satellite Data & AI
In this video we explain how Dimitra's AI team led by David Rivas uses satellite data and artificial intelligence (AI) for precision agriculture.
The process starts by collecting satellite images from Landsat, Sentinel, and Planet, including open-source and high-resolution data. These satellites capture a broader spectrum of light than the human eye can see. They do not just capture regular pictures, but can also detect wavelengths of light invisible to the human eye, such as near and intermediate infrared light. These images help us assess vegetation health and soil properties.
We utilize various sensors on satellites to collect sunlight reflecting from the Earth, analyzing this data through mathematical formulas to generate human-readable data sets called satellite indices. These indices give us information on various aspects of agricultural health, such as vegetation conditions, moisture levels, and soil nutrients. By mapping these indices onto heat maps, farmers can visually identify areas of their farms that require attention, such as where moisture is deficient or nutrients are lacking.
By applying AI to solve problems identified by these satellite images, such as irrigation or nutrient application, precision agriculture allows for more efficient and economical farming practices. Precision agriculture allows farmers to use resources more efficiently. By targeting specific areas of their fields that need attention, they reduce waste and increase crop yields. This smart farming approach leads to more sustainable agriculture by minimizing environmental impact and enhancing food security for a growing global population.
Dimitra’s platform uses historical satellite data and AI to track and predict the evolution of crop health throughout the season. This predictive analysis helps preemptively address potential issues, thus optimizing crop yields.
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