TL;DR:
- TrailGuard AI, developed by Resolve, utilizes AI-enabled cameras to detect and transmit images of specific species, aiding conservation efforts.
- Originally designed to combat poaching, TrailGuard AI is now being used in India to manage human-tiger conflicts.
- The technology recognizes up to 10 species, conserving energy and allowing cameras to operate for over two years without battery changes.
- In a trial, it protected communities by sending real-time alerts when tigers were nearby, reducing retaliatory killings.
- The accuracy of TrailGuard AI cameras is impressive at 98.8%, making them a valuable tool in tiger conservation.
- Resolve has upgraded the technology for even better performance and is commercializing it through Nightjar, with pre-orders from wildlife habitat management companies.
Main AI News:
Amidst the lush jungle, a tiger stealthily maneuvers through tree trunks and foliage, its distinctive stripes melding seamlessly with the dappled shadows beneath the forest canopy. This elusive creature, elusive even to the keenest human eye, poses a significant challenge to those seeking to protect it. However, artificial intelligence (AI) has emerged as a game-changing ally in the fight for tiger conservation.
TrailGuard AI, developed by the US-based NGO Resolve, represents an innovative leap forward in wildlife preservation. This advanced camera trap is specifically engineered to detect and instantly transmit images of specific species, making it a valuable tool in the realm of conservation.
Originally designed to combat rampant poaching, TrailGuard AI made its debut in an East African reserve in 2018. In that inaugural field-test, the technology played a pivotal role in the apprehension of 30 poachers. Recognizing its potential, conservationists in India adopted this AI marvel to address human-tiger conflicts.
Powered by a sophisticated vision chip embedded with AI, TrailGuard AI can discern up to 10 different species, including tigers, leopards, elephants, and humans. What sets it apart is its real-time data transmission capability, allowing it to relay information to park rangers via cellular networks or long-range radio. This efficiency, coupled with its selective species recognition, significantly reduces its energy consumption, enabling it to operate continuously for over two years without requiring frequent battery replacements.
AI in Action: Protecting Tigers and Communities
In a recent trial conducted in India’s “tiger state” of Madhya Pradesh, TrailGuard AI deployed 12 cameras in the Kanha–Pench corridor. This expansive landscape, encompassing the Pench Tiger Reserve and the Kanha Tiger Reserve, serves as a haven for over 300 tigers, forming the largest population in central India. While this abundance of tigers is a testament to the region’s thriving ecosystem, it also places the local human population at risk.
With approximately 600,000 people residing in 715 villages within the corridor and an additional 2.7 million people living within a five-kilometer radius, human-tiger conflicts are inevitable. One prevalent issue is tigers preying on livestock, which translates to financial losses for villagers and, at times, leads to retaliatory killings, further endangering the already vulnerable tiger population.
TrailGuard AI’s swift data transmission mechanism comes to the rescue of these communities. When the camera captures an image of its target species, it promptly relays the information, including location, detection time, and the species identified, to forest rangers via email and instant messaging apps. This early warning system equips villagers with crucial information, allowing them to respond effectively to the presence of a tiger, even if it’s just 300 meters away.
In instances where livestock falls victim to tiger attacks, the captured images serve as valuable evidence for villagers to claim compensation from authorities, expediting the payment process. Consequently, this technology fosters greater tolerance among communities living alongside these apex predators.
Himmat Singh Negi, the former director of Kanha Tiger Reserve, lauds the transformative impact of TrailGuard AI. Witnessing tangible results, he notes that the technology has empowered ground-level conservation efforts and averted potentially disastrous situations.
Addressing the Growing Need for Conservation Technology
The demand for technology that mitigates human-wildlife conflicts is on the rise. Globally, human populations around tiger conservation areas increased by 19.5 million people between 2000 and 2020, with 35% of India’s tiger population permanently residing outside designated reserves.
TrailGuard AI underwent a second successful trial at Dudhwa Tiger Reserve, a protected area spanning 1,310 square kilometers with approximately 107 tigers. During this trial, the technology led to the apprehension of four poachers attempting to infiltrate the forest under the cover of darkness, underscoring its effectiveness.
The results of these trials, published in the peer-reviewed journal BioScience, revealed an impressive accuracy rate of 98.8%, marking the first instance of an AI-enabled camera transmitting images of a wild tiger.
To further enhance its capabilities, Resolve has upgraded the vision chip in TrailGuard AI cameras, promising increased accuracy and faster performance. These improved cameras will soon be deployed in Kanha-Pench and Dudhwa reserves, as well as in West Bengal state, where they will be employed in a new trial aimed at managing human-elephant conflicts.
TrailGuard AI is now transitioning into the commercial sphere through a spinout company called Nightjar, with plans to produce 500 units by March 2024. Encouragingly, Nightjar has already secured pre-orders from companies responsible for managing wildlife habitats.
A Brighter Future for Tigers and Communities
As apex predators, tigers play a crucial role in maintaining the delicate balance of forest ecosystems, which in turn sustains and supports the livelihoods of numerous communities. Piyush Yadav, a conservation technology fellow at Resolve, envisions a future where tigers and local populations thrive in harmony, thanks to TrailGuard AI.
He emphasizes that villagers understand the vital importance of tigers in their ecosystem, acknowledging that the coexistence of both species is the ultimate goal of their conservation efforts. TrailGuard AI, with its real-time alerts and data-driven approach, stands as a beacon of hope in achieving this delicate equilibrium.
Conclusion:
The adoption of TrailGuard AI in India signifies a significant advancement in wildlife conservation and conflict management. Its accuracy, energy efficiency, and real-time alerts have the potential to transform the market for conservation technology, attracting interest from both environmental organizations and wildlife habitat management companies seeking innovative solutions to protect endangered species and foster coexistence between humans and wildlife.
