The Harvest Africa initiative leverages world-class technical expertise, scaling and applying machine learning and EO-based data and tools across East and Southern Africa in addition to supporting operational EO-based agricultural monitoring. We aim to advance innovative satellite-driven methods to produce automated within season crop type and crop-specific condition products that support agricultural assessments. We directly involve national and regional agencies in the development process to ensure smooth transition to the respective government agency or partner.
|Status and Opportunities for Tanzania Agrometeorological Services||This project supports the Tanzania Meteorological Agency (TMA) in improving their capability to deliver actionable agrometeorological data products to farmers and other end users.|
|National Crop Monitors||A combined effort from multiple projects supporting national agencies to develop and maintain Earth observations-based agricultural monitoring systems.|
|Earth Observation for National Agricultural Monitoring||This project aims to advance national agriculture monitoring with Earth Observations (EO) data in East and Southern Africa using machine learning tools and open source data to develop baseline datasets.|
|Earth Observations for Field Level Agricultural Resource Mapping (EO-FARM)||The EO-FARM project is a collaboration with Swiss Re Foundation, using Earth observations data to enhance food security and resilience in small-holder dominated regions by revolutionizing fundamental datasets needed for agricultural monitoring and enhancing government Crop Insurance programs.|
|Relief 2 Resilience in the Sahel||Lutheran World Relief is working with NASA Harvest in Mali to gather valuable on-the-ground information about crop conditions so that relevant government agencies can better interpret satellite imagery and advise farmers about potential challenges. The Relief to Resilience in the Sahel (R2R) project will help more than 8,200 farming families in Burkina Faso, Mali and Niger recover from devastating food crises and better prepare for future challenges.|
|Mapping production and loss using satellite data and drones||IFPRI and UMD are working as partners to understand how satellite data can help us better understand crop production and loss in Tanzania, using drones for ground truthing.|
|Helmets Labeling Crops||
This project will create unprecedented ML-ready labeled datasets for crop type, crop pest and disease, and market prices in the main food production regions in five African countries. The team will use novel and innovative approaches that include rapid point data collection with cameras mounted on the hoods of vehicles—“helmets”—combined with crowdsourcing to create point and polygon labels. By partnering with local universities, this project will create opportunities for training future African researchers to use remote sensing and machine learning.