Monitoring and modelling the parasitic weed Striga in Africa

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Technologies for rapid acquisition and analysis of data on the occurrenceof pests and diseases offer the potential to revolutionise global agriculture.In this project we will use the latest tools for collecting data alongsidecutting-edge data science to help manage a major weed that threatens cropproduction and livelihoods across Africa.Our research has developed techniques for monitoring and modelling pestpopulations at large-scales, and we use such data for predicting futureinfestations. Initially based on ecological monitoring techniques, we nowuse drone and satellite data for large-scale acquisition of data onpopulations of weeds.In this project we will apply these approachs to the weed Striga('witchweed') which infests over 40% of rice and maize crops acrossAfrica, affecting over 100 million people. We propose to address 3significant problems:(1) Monitoring: we will develop a pipeline that integrates severalmethods for data collection, including surveys, citizen science, UAVs andsatellite imagery.(2) Models: forecast infestations in the future under alternativemanagement.(3) Communication: integrate analysis and outputs into a platform thatcan be used to map current distributions, as well as to forecast futureinfestations.Desirable skills include a selection from: ecological monitoring,experience with drones, statistics and programming.