Kuzi—the Swahili name for the wattled starling, a bird renowned for eating locusts—is an AI-powered tool, created by Selina Wamucii, that uses satellite data, soil sensor data, ground meteorological observation, and machine learning to predict the breeding, occurrence, and migration routes of desert locusts across the horn of Africa and Eastern African countries. Kuzi uses a machine-learning model trained on the satellite data of soil moisture, wind, humidity, surface temperature, as well as on soil sensor data and the vegetation index – all factors that
affect the breeding, swarm formation, and movement of locusts. Kuzi displays a heat map of high-risk areas along with forecasts of breeding, swarm formation, and migratory attacks that can alert farmers and pastoralists to potential locust activity some 2-3 months in advance of the event. They can also receive SMS alerts when locusts are highly likely to attack farms in their areas, including plants for livestock.
A real-time situation of locusts across Africa, currently for Ethiopia, Somalia, Kenya, and Uganda.
Shows all potential routes for the locust swarm movement
A real-time locust breeding index based on satellite data, ground meteorological observation data, and machine learning
Sign up to receive Free SMS alerts when your area is in danger of a locust swarm attack.