Introduction

Human-elephant conflict is a persistent challenge in India, particularly in regions where railway lines intersect traditional migratory routes of elephants. In response, the Tamil Nadu Forest Department, in collaboration with the railways, has pioneered an AI-enabled early warning system in the Walayar–Madukkarai forest range along the Kerala–Tamil Nadu border. This initiative, operational since February 2024, has emerged as a model for technology-driven wildlife conservation.

Background

The Western Ghats, a UNESCO World Heritage Site and a critical biodiversity hotspot, serves as a migratory corridor for elephants moving between the Nilgiris, Sathyamangalam, and Kerala forests. However, expanding rail and road infrastructure, coupled with land-use changes and increasing human presence, has fragmented these habitats.

The Coimbatore Forest Division alone reported nearly 9,000 elephant straying incidents between 2021 and 2023. The Madukkarai range, traversed by two railway tracks through reserved forests, has been particularly vulnerable. Since 2008, 11 elephants, including calves, have died due to train collisions in this area, underscoring the urgency for preventive measures.

The AI-Powered Surveillance System

• Infrastructure: The system comprises 12 surveillance towers fitted with 24 high-resolution thermal cameras, strategically placed along a vulnerable 7-km stretch of railway track.
• Technology: Using artificial intelligence and machine learning, the cameras detect elephant presence up to 100 feet near tracks. The system is modeled on surveillance technology used by the Indian Army at border areas.
• Command Centre: Located near Walayar, it is staffed by trained forest personnel and local tribal youth. The system operates 24×7, sending real-time alerts to railway control rooms, loco pilots, and patrol teams.
• Integration with Railways: Alerts prompt loco pilots to slow down or halt trains, while forest staff guide elephants safely across or divert them through designated underpasses.
• Results: Since its commissioning, the system has facilitated over 6,500 safe elephant crossings and generated over 5,000 alerts, with zero elephant fatalities reported.

Broader Significance

1. Wildlife Safety: The system ensures unhindered migratory movement of elephants, mitigating human-wildlife conflict.
2. Community Involvement: Employment of tribal youth in monitoring enhances local participation in conservation.
3. Judicial Oversight: The initiative aligns with the Madras High Court’s 2021 directive to minimize elephant deaths on railway tracks.
4. Multi-Species Benefit: The cameras also detect human presence, reducing risks of accidents and aiding in conflict management.

Challenges and Way Forward

Elephants are highly adaptive and often circumvent traditional barriers such as trenches or solar fences, making AI-driven monitoring more reliable. However, scaling up faces challenges of financial allocation, technical expertise, and maintenance. The Tamil Nadu government has announced plans to expand the system to four more vulnerable areas including Dharmapuri and Hosur, indicating policy commitment.

To ensure long-term success:

• Similar AI-based systems should be introduced across critical elephant corridors in states like Assam, Odisha, and Karnataka.
• Railway infrastructure should integrate wildlife underpasses and overpasses with AI monitoring.
• A national-level database linking forest departments and railways could enhance coordination.
• Awareness campaigns and community engagement must complement technological solutions.

Conclusion

The AI-powered early warning system in Coimbatore division demonstrates how advanced technology, when integrated with local knowledge and institutional support, can resolve persistent conservation challenges. With elephants being a keystone species vital to ecosystem stability, such interventions are crucial to balance developmental imperatives with biodiversity conservation. Replicating and scaling up this initiative across India could significantly reduce human-elephant conflict and serve as a global model for AI-driven wildlife protection.