Fatima Zaidi1, Hamid Laga1, Ferdous Sohel1, Mahmood Golzarian1, Prof. Lori Lach2, Ben Hoffmann3, Chris Burwell4, Melissa L Thomas1

1Harry Butler Institute, Murdoch University, Australia, 2James Cook University, Centre for Tropical Biosecurity, Cairns, Australia, 3CSIRO, Health and Biosecurity, Winnellie, Australia, 4Queensland Museum, South Brisbane, Australia

Biography:

Professor Lach is internationally recognised for her 20+ years of ant ecology research, particularly her work on invasive ants. Her research encompasses the ecology, impact, detection, and control of invasive species. She co-leads several projects researching new technology to improve invasive ant detection. She is the Director of the Centre for Tropical Biosecurity at James Cook University. She was honoured to be the recipient of the Ian Mackerras Medal in 2022.

Abstract:

Invasive ants are one of the most serious biosecurity risks in Australia. Six of seven high priority invasive ants present in Australia are ranked among the most notorious invasive pests globally. Early detection and eradication are crucial for limiting the impact of these ants, however current practices for identifying invasive ants are time and labour intensive and rely on ever shrinking taxonomic expertise. To help non-specialists determine if an ant is likely to be one of seven high priority species, we have developed an ant identification platform. The platform uses a hierarchical deep learning-based machine learning approach to identify seven invasive ant species that pose a high risk to Australia. The machine learning algorithm has been trained on over 200,000 individual ants from 15,000 images collected from across Australia. Our extensive tests show that the algorithm currently provides 90% or greater correct predictions of the seven target species. Citizen scientists will be able to access this tool using a mobile application on both Android and iOS devices.