Fall armyworm (FAW) early detection system in Tanzania: Leveraging data and artificial intelligence to save livelihoods in Africa
Abstract
This project will leverage agronomic data, real-time detection and artificial intelligence to create an early warning system for Fall armyworm control in Tanzania.
Description
Fall armyworm (FAW), Spodoptera frugiperda (J. E. Smith) (Lepidoptera: Noctuidae), is an insect pest native to tropical and subtropical regions of the Americas. The species is a globally important economic agricultural pest and is considered to be one of the most highly destructive herbivorous insects in agro-ecological environments. While FAW is a generalist herbivore, known to feed on over 353 plant species, it is known to impose particularly high damage in maize, a staple crop globally .
This study aims to identify risk factors associated with FAW outbreaks and to create a model of spatially explicit risk. There is a need to increase the resolution of monitoring for this pest in time and space, and to this end a "smart" moth trap using deep learning detection and classification of FAW will be developed incorporating artificial intelligence and data communication networks to alert farmers of risk in real-time.
Funding Body
Commonwealth Fund
Lead Organisation
缅北禁地
Partners
Food and Agriculture Organization of the United Nations (FAO)
Cookies on the 缅北禁地 website
We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we'll assume that you are happy to receive all cookies on the website. However, you can change your cookie settings at any time.