Testing limitations and developing guidelines for the use of remote sensing technology across airborne platforms for weed detection in mixed landscapes.
This project will:
Study sites have been identified within three different landscapes across NSW housing the three model species:
1) Kosciusko National Park – Hawkweeds
2) Snowy Monaro region – African lovegrass
3) North and South Coast – Bitou bush
Site ground surveys will be undertaken four times at various plant phenological stages to facilitate recording of site vegetation and landscape features. These will correspond with all imagery taken simultaneously at the same sites.
The relationships between accuracy and spatial resolution for sites will reveal limits of detection in relation to operational parameters. Machine learning and image processing will determine detection limitations and optimal spatial scales for each weed system.
A set of guidelines will be developed from project results to provide broad instructions on the use of remote sensing technology for weed detection, and for application to other weed species and landscapes.
The online portal and community of practice will be developed to facilitate data and resource sharing, networking and support for end users within the remote weed detection community..
It brings together drone, machine learning and remote sensing researchers and weed experts from Charles Sturt, the Queensland University of Technology, and weed management experts from NSW National Parks and Wildlife Service.
This project is supported through funding from the Australian Government’s Established Pest Animals and Weeds Management Pipeline Program - Advancing Pest Animal and Weed Control Solutions
Other partners include South East, North Coast and Murray Local Land Services, Mid Coast, Eurobodalla and Bega Valley Shire Councils, the Illawarra District Weeds Authority, and XAg Australia.