Oral Presentation Science Protecting Plant Health 2017

Optimising surveillance protocols using unmanned aircraft systems: the power of perspective (4121)

Brian McCornack 1 2 , John Weiss 2 3 , Felipe Gonzalez 2 4 , Grant Hamilton 2 4 , Geoff Pegg 2 , Jonathan Kok 2 4 , Ganesh Bhattarai 1 2
  1. Kansas State University, Manhattan, Kansas, United States
  2. Plant Biosecurity Cooperative Research Centre, Canberra, Australian Capital Territory, Australia
  3. Department of Economic Development, Jobs, Transport and Resources, Victoria Government, Bundoora, Victoria, Australia
  4. Queensland University of Technology (QUT), Brisbane, Queensland, Australia

Our project focuses on modern remote sensing technologies for surveillance and monitoring organisms that threaten plant biosecurity across broad spatial scales. The future of effective and efficient biosecurity surveillance programs, and pest management in general, will require a higher level of automation and technical sophistication and an increased dependence on affordable technologies. Reliable yet effective sampling efforts are imperative to the future of plant biosecurity and food security in general. Our team investigated sensitivities and capacity of emerging, small unmanned aircraft systems (sUAS) and imaging technologies, including multi- and hyperpsectral sensors, for biosecurity surveillance in viticulture, horticultural and grains industries. The overarching aim of this project is to investigate the use of these technologies to support claims of pest freedom and low pest prevalence compared to commonly deployed surveillance practices and utility to inform pest management decisions for established species. The project focuses on the use of science-centric data (e.g., using pest biology to identify areas most likely to be infested) to inform surveillance decisions (e.g., when and where to deploy a UAS equipped with multi-spectral cameras) made by biosecurity personnel and pest managers. This project also aims to determine how such technologies could improve detection rates and/or surveillance efficiency at the regional, field/orchard or plant level using case studies.