Monitoring and modelling the parasitic weed Striga in Africa

病虫害发生数据的快速获取和分析技术提供了改变全球农业的潜力。在这个项目中，我们将使用最新的数据收集工具以及尖端数据科学来帮助管理威胁整个非洲农作物生产和生计的主要杂草。我们的研究开发了大规模监测和建模害虫种群的技术，我们使用这些数据来预测未来的虫害。最初基于生态监测技术，现在我们使用无人机和卫星数据来大规模获取杂草种群的数据。在这个项目中，我们将把这些方法应用于杂草Striga(独脚金)，它侵袭了整个非洲40%以上的水稻和玉米作物，影响了超过1亿人。我们建议解决三个重大问题：(1)监测：我们将开发一条整合多种数据收集方法的管道，包括调查、公民科学、无人机和卫星图像。(2)模型：在替代管理下预测未来的虫害。(3)沟通：将分析和输出整合到一个可用于绘制当前分布以及预测未来虫害的平台。可设计的技能包括：生态监测、无人机经验、统计和编程。

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.