Optimising Surveillance Protocols using Unmanned Aerial Systems
The aim of this project is to use predictive models combined with high-resolution detection technologies to increase sampling efficiency and improve detection rates of important pest species that pose a threat to national biosecurity. In conjunction with the Kansas State University, Hamilton Ecology Lab is developing specific protocols for optimising detection of important invasive species such as stripe rust (Puccinia striiformis) in winter wheat crops. this will be achieved through a series of objectives outlined below:
1) Modelling region-wide environmental changes to identify criteria for selecting high-risk surveillance areas and compare these predictors to current selection methods deployed by biosecurity personnel.
2) Prioritise sampling times and areas within targeted areas to direct surveillance efforts and increase rate of first detection using higher-resolution surveillance technologies (fixed-wing UAVs) and unique spectral signatures.
3) Evaluate utility of higher-resolution cameras and robotic technologies on multi-rotor UASs to categorise and/or collect target pests on different plant structures for identification by trained diagnosticians.
4) Synthesise modelling and improved UAS technologies to demonstrate a practical application for surveillance of high priority plant pests in commercial crops.
This project is due for completion in December 2017. More information on this project is available here
Stripe rust (Puccinia striiformis) on wheat. Photo by Yue Jin.