A major bottleneck to the application of machine learning tools to satellite data of African farms is the lack of high-quality ground truth data. Here we describe a high throughput method using youth in Kenya that results in a cost-effective method for high-quality data in near real-time. This data is presented to the global community, as a public good and is linked to other data sources that will inform our understanding of crop stress, particularly in the context of climate change.
All Science Journal Classification (ASJC) codes
- Global and Planetary Change
- Food Science
- Agronomy and Crop Science
- Management, Monitoring, Policy and Law