Funded by the Lacuna Fund, Helmets Labeling Crops is creating 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. 

Funded by NASA SERVIR, the main objective of this project is to advance national agriculture monitoring by using Earth Observations (EO) data in East and Southern Africa through the development of in-season semi-automated baseline datasets derived from scalable machine learning tools and open data, both of which are needed for more accurate agriculture monitoring.

Funded by the SwissRe Foundation -The goal of this work is to develop semi-automated, scalable remote sensing-based datasets and information products for maize and wheat conditions and yield assessment. First, working with Kenya’s Ministry of Agriculture, Livestock, and Fisheries (MoALF) and county governments, the team will use the EO-derived dataset (within-season crop maps) required for yield assessments to guide sample design to enhance crop conditions and yield assessments. The products can reduce the cost of yield assessment, accelerating the process to ensure early pay-outs to the Crop Insurance Programme.