Our applications use spectral analysis and radar-based analysis from the satellite to provide us with readings on the ground. This data is combined with weather, planting, soil data, and known agronomy parameters, and a combination of statistical analysis and artificial intelligence is used to make recommendations to maximize the performance of each crop. These models are built for each crop and each combination of soil, terrain, weather, irrigation, and known soil data. Machine learning is used to compare data sets across the world and align those recommendations based on regional differences.
Crop performance is a feature of the Connected Farmer Platform. Farmers are available to license with payments in DMTR. Farmer rewards from licensing and use are locked for a fixed period of time prior to release back to the cooperative.
The platform analyzes agricultural data from farmer inputs, satellite images, value chain partners, and IoT devices and uses statistics and AI to deliver insights and recommendations to farmers so they can improve crop performance and overall profits. The platform connects national agriculture data to help governments generate reports and organize local agricultural value chains, providing traceability and data to develop better public policies and increase GDP.
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