This project will create livestock datasets for disease identification, geospatial epidemiological modelling, and rapid point-of-care diagnostics in low-resourced rural remote areas. The datasets will unlock research for the livestock sector and reduce the cost of access to accurate infield diagnosis with model deployment to mobile smartphones. This will create opportunities for mass livestock screening even in the most remote herding regions that are severely limited by costly cold chain logistics to highly capable laboratories. In addition, the datasets of geotagged and expert-labelled symptomatic images will form the basis of real-time symptomatic surveillance mapping, enabling national-level livestock agencies to monitor the incidence and severity of symptoms/disease at scale and inform evidence-based intervention planning.

With support from Lacuna Fund, the joint efforts of Makerere AI Lab, Makerere Molecular Lab, and Veterinarians Without Borders will pave the way for open datasets for machine learning, an important contribution to innovation for the resilience of the livestock sector in Africa and emerging economies at large.

Datasets

Library

Models