The launch of the Sentinel satellite family has increased the availability of data, especially in the imagery domain, at no or low cost to the users. Totake advantage of these data, we propose to experiment the combination of remote sensing data (satellite imagery) with machine learning methods and agronomic knowledge to provide a package of digital agricultural extension support to “smallholders”, in developing countries: – identification of crops seeded on fields; -estimation of crops’growth stages (emergence specially); – monitoring of cropping activities; -dissemination of timely, localized and individualized agronomic recommendations; – verification of crop lands usage; – verification of information provided by remote smallholder farmers about crops seeded on fields; – mapping land usage in areas with no information from the smallholder farmers about crops seeded on fields. The approach is applied to cotton and others crops such as casava, rice and corn.
|Voucher||Business and Technology maturation|
|Type of proposal||Collaborative|