Crop detection in our project model is not restricted to the traditional data of crop type and rainfall only, it will create a library of crops based on key information such as crop classification, crop health, soil type and rainfall.
Food@Home is a platform for crop yield prediction using recent techniques in AI and machine learning. Food@Home uses free multi spectral imagery from the Sentinel 2A satellite (European Satellite agency) as well as relevent paremters from NASA’s satellites (GPM, MODIS etc) and ground truthing using our smartphones based platform. Food@Home is capable of giving accurate crop yield prediction at 10 metres resolution and up to 1 week frequency. Using Food@Home you can find out the type of crop cultivated and its current yield and health on a weekly basis. The project is currently focused on the province of Punjab in Pakistan, where 3300 crop reporting survey officers have been been equipped with smart Phones for ground truthing. The project builds on the volunteer computing platform SETI@Home to scale up to other parts of the world. Find out more about our models for crop classification and crop yield prediction.
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Learn all about Food@Home and how we work in the links provided.