Background

Peter Ngimbwa is a PhD student at Harper Adams University conducting research on developing an early detection system for Fall Armyworm (FAW) outbreaks in Tanzania through the integration of artificial intelligence and real-time monitoring technologies. His research project aims to identify risk factors associated with FAW outbreaks and create a model of spatially explicit risk, leveraging agronomic data, real-time detection and artificial intelligence to create an early warning system for Fall armyworm control in Tanzania. Working under the supervision of Dr. Richard Green, Dr. Edwin Harris, Professor Simon Leather, and Dr. Sven Peets, his work addresses the urgent need for improved pest monitoring of this devastating agricultural pest that has caused billions of dollars in crop losses across Africa since its arrival in 2016.

Ngimbwa’s research focuses on developing a “smart” moth trap using deep learning detection and classification of FAW, incorporating artificial intelligence and data communication networks to alert farmers of risk in real-time. This innovative approach seeks to increase the resolution of monitoring for Fall Armyworm in both time and space, providing Tanzanian farmers with critical early warning capabilities to protect their maize crops. His work is supported by funding from the Commonwealth Fund, Harper Adams University, and the Food and Agriculture Organization of the United Nations (FAO), reflecting the international significance of developing sustainable pest management solutions for this invasive species that threatens food security across sub-Saharan Africa.