In 2019, the conservation nonprofit Australian Wildlife Conservancy started developing an artificial intelligence model to identify animals in camera-trap images. Since then, despite limited funding and resources, the team has managed to train the model to recognize 44 species, from native dingoes, kangaroos and woylies, to introduced predator cats and foxes. Now, thanks to a funding grant from the Australian government, the project is getting a boost that will enable it to almost triple the model’s capabilities. The A$750,000 ($492,000) grant from the government’s Innovative Biodiversity Monitoring Grants Program will help AWC further develop the model to recognize and identify up to 120 native species. “The funding will allow us to employ additional staff to gather the source data, train and test the models, and purchase additional AI-processing power in the cloud,” Damien Kerr, chief information and technology officer at AWC, told Mongabay in an email interview. Camera traps have long been used for wildlife tracking and monitoring. Even as newer and more cutting-edge technology have been developed and deployed globally, camera traps continue to be one of the most widely used tools for biodiversity surveys. However, as their capabilities have increased over the years, so has the amount of data they gather in a short span of time. For scientists and researchers, analyzing and processing these images to identify individual species remains a major bottleneck. Not only is the process laborious and time-consuming, but it also requires expertise to recognize the animals accurately. “While species like koalas and echidnas…This article was originally published on Mongabay
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