Press Release

Federal grant to fund AI-supported wildlife recognisers

13 Feb. 2024
AWC

Australian Wildlife Conservancy (AWC), a global leader in conservation, has secured $750k in federal grant funding to develop and train Artificial Intelligence (AI) models to accurately recognise and identify up to 120 native wildlife species.  

Funds from the Australian Government’s Innovative Biodiversity Monitoring Grants Program will be used to create open-source AI recognisers which will assist in effectively processing camera trap data collected during biodiversity surveys across Australia.  

 

AI has the potential to improve the speed of camera trap image processing but widespread application is challenging due to the number of high-quality target species images required to accurately train the AI model. AWC
AI has the potential to improve the speed of camera trap image processing but widespread application is challenging due to the number of high-quality target species images required to accurately train the AI model.

 

Although camera traps have revolutionised biodiversity monitoring, a single camera can collect thousands of images and ecologists will spend days, sometimes months, manually reviewing each image bank. AI has huge potential to improve the speed and cost effectiveness of camera trap image processing. However, widespread application of AI is challenging due to the number of high-quality target species images which are required across a range of habitats to accurately train the AI model.  

In preparation for the AI project, AWC’s National Science Team has started collating images from the organisation’s extensive library, collected over the last 15 years from sanctuaries and project partners. Data Scientist and Software Developers in AWC’s IT team have also developed a camera trap processing and reporting pipeline and tested methods by successfully creating AI recognisers for 44 species to date.  

AWC aims to develop species recognisers for 120 mammals and reptiles including the endangered Northern Bettong, the near threatened Western Quoll, the vulnerable Great Desert Skink, and the Yellow-spotted Monitor. The Species Classifier model(s) will be made available on a public open-source repository (example GitHub), along with documentation on how to use the model for animal classification, as well as documentation on AWC’s camera trap image processing pipeline, methods and tools.  

 

AWC has trialled AI recognisers to identify cats and aims to develop species recognisers for 120 mammals and reptiles. AWC
AWC has trialled AI recognisers to identify cats and aims to develop species recognisers for 120 mammals and reptiles.

 

Tim Allard, AWC Chief Executive Officer, welcomed the funding.  

“AWC is increasingly turning to emerging technologies to improve efficiency and the quality of data collected in the field,” Allard said. “By harnessing the power of AI, these species recognisers will provide an accurate and cost-effective method for monitoring changes in biodiversity across Australia. It will also help free our ecologists to spend more time in the field, where they have the biggest impact.”  

“This project represents excellent value for government investment as it builds on AWC’s existing sanctuary and data collection infrastructure, bolstered by an established network of expert ecologists familiar with the different fauna assemblages of each region. These resources cannot be replicated without millions of dollars of investment and years of effective project planning.” 

For more information on AWC’s work with AI and other advanced technologies, click here.

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This project received grant funding from the Australian Government. 

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