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While biodiversity and wildlife may not immediately spring to mind when considering AI, conservation agencies have long employed a range of technologies to monitor and ensure the well-being of ecosystems and wildlife.
As per research, the market for AI in forestry and wildlife was estimated to be worth US $1.7 billion in 2023. It is projected to expand at a compound annual growth rate (CAGR) of 28.5% to reach US $16.2 billion by 2032.
Let’s look at some of the top use cases of AI in wildlife conservation.
AI-Powered Wildlife Monitoring
Conventional techniques frequently depended on manual observation, which was labour-intensive and liable to human mistake. AI-powered monitoring systems with cutting-edge sensors and cameras, help address this.
Real-time tracking, identification, and detection of animals by these technologies can gather information about their habitat preferences, and population dynamics. Large-scale datasets are analysed by machine learning algorithms, which allow researchers to derive meaningful insights.
For instance, wildlife officials track the movement of animals in the Kanha-Pench corridor in Madhya Pradesh using the TrailGuard AI camera-alert system.
It runs on-the-edge algorithms to detect tigers and poachers and transmit real-time images to designated authorities responsible for managing prominent tiger landscapes.
Guardians of the Wild
Many national parks have installed camera traps – or cameras with infrared sensors, deployed in forests to monitor the movement of potential poachers – that harness the power of AI.
Recently, Susanta Nanda, a wildlife enthusiast and an Indian Forest Service (IFS) officer, recently shared images of intruders captured by an AI-enabled camera at Similipal Tiger Reserve in Odisha on X. This quick response time, made possible by AI, not only helped apprehend intruders but also deterred potential poachers.
Indian Forest Officer Sushant Nanda. Source: X
AI-based surveillance systems will soon be equipped in elephant corridors across the country by the name Gajraj.
Species Identification
Using AI for camera detection. Image source: X/@ai_conservation
The Wildbook project uses AI in species identification. AI algorithms are used for identifying specific animals based on their distinct physical qualities, such as the pattern of spots on a giraffe or the form of a whale’s tail. The time and effort needed by scientists for species identification are greatly decreased by this automated method
Satellite Imagery to Track Endangered Wildlife
SilviaTerra (now known as NCX) creates comprehensive maps of woods by analysing satellite pictures. These maps offer important information about the kinds of trees found there, how well-maintained the forests are, and how much carbon they can store. To manage forests in a way that lessens the effects of climate change, this information is essential.
An Eagle’s Eye for The Wild
Traffic, a well-known non-governmental organisation that works on the worldwide trade in wild animals and plants, have created an AI programme that analyses internet data about the trade in wildlife.
The “AI Wildlife Trade Analyst” an AI tool can interpret enormous volumes of data from many internet sources, such as social media, online forums, and e-commerce platforms. Information about wildlife commerce, including species names, items, prices, and locations, is identified and categorised. The data is then utilised to produce insights regarding the trade’s scope, makeup, and patterns.
PATTERN, which was created with the aid of Microsoft Azure AI Custom Vision, is an end-to-end computer vision platform and AI service that offers a user-friendly interface for labelling photos.
Habitat Analysis
The High-Resolution Land Cover Project of the Chesapeake Conservancy used AI to produce a high-resolution map of the watershed of the Chesapeake Bay, which is roughly 100,000 square miles. Compared to traditional 30-metre resolution land cover data, the map’s one-metre resolution offers 900 times more information. It’s important to note that implementing AI technologies in wildlife conservation can be costly and may require significant technical expertise. Despite these challenges, AI’s benefits and potential applications in wildlife conservation are vast and promising.
