Monitoring and modelling the parasitic weed Striga in Africa

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