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The Role of AI in Wildlife Conservation

The tech for good sector — comprising a wide range of health, education, cleantech, femtech and enterprise environmental, social and governance (ESG) software and services — is worth around $79 billion globally. However, it’s not surprising that TFGI’s recent poll of 14,000 people indicated a clear lack of confidence in the sector to achieve its promise of transformative impact. This is partly because the broader ecosystem of tech for good must address complex problems that go beyond optimising efficiency or simply preventing harm, and involve profound changes in goals, mindsets, practices and behaviours.

The use of AI for wildlife conservation offers a potential way to address some of these challenges. Using sensors and cameras, AI is able to monitor wildlife in natural habitats, alerting conservationists of any changes. This can help to protect animals from threats such as poaching and climate change.

It is also able to identify the many different species of animal, which can be difficult for humans, due to their overlapping appearances in the wild. This can aid in the identification of tracks, dung and other marks, allowing them to be numbered and tracked, which is useful for monitoring population trends. It is also useful for detecting the presence of wildlife in a location, which can assist in identifying areas of land suitable for protection or where conservationists should concentrate their efforts.

Using drones technology website equipped with AI-enabled sensors, it is possible to monitor large areas of land more accurately. This makes it easier to track the movements of wildlife and detect any signs of poaching, which can then be reported to authorities. Moreover, it is possible to track the movement of elephants and other large mammals using AI-enabled sound surveillance, enabling the detection of any illegal activities such as deforestation or herding.

One example of AI-enabled technology for wildlife conservation is the open source Microsoft AI for Earth MegaDetector model, which works off-the-shelf to automatically process images from camera traps and has been integrated into the workflow of numerous organizations. This can dramatically speed up data processing, analysis and interpretation and help to inform conservation policies.

Another application of AI in wildlife conservation is the elephant listening project, where researchers and developers used neural networks – a subset of artificial intelligence – to separate jungle noises and distinguish between elephant calls and other sounds. This can help to predict the most effective routes for rangers to patrol, and where poachers are likely to move.

Similarly, an AI-enabled system called PAWS has been developed to aid rangers in combating poaching in Kenya’s dense forests. It uses audio recordings of tech ogle elephant calls and other jungle noises, as well as satellite tracking and information from a community-based monitoring network called SMART to locate animals and poachers. The system then sends a notification to the nearest ranger, who can act accordingly. This has been a successful method of helping to thwart the poaching of endangered elephants and other large mammals.