The Tamil Nadu Environment, Climate Change & Forests Department has emerged as a national frontrunner in deploying artificial intelligence and machine learning for conservation governance. Supriya Sahu, IAS, Additional Chief Secretary, in an exclusive conversation with Bhaswati Guha Majumder, Associate Editor at APAC Media, explains how the state has operationalised AI-enabled real-time monitoring along high-risk railway corridors to reduce wildlife fatalities, institutionalised a technology-driven Command Centre in Gudalur for interdepartmental coordination and built a scalable, accountable framework that integrates sensors, predictive analytics, and data governance into frontline conservation and climate resilience efforts.
Tamil Nadu has emerged as a frontrunner in deploying AI and ML to mitigate human–wildlife conflict. What governance gaps were these technologies designed to address, particularly along railway corridors and how have outcomes improved so far?
AI and ML were introduced to strengthen governance efficiency in high-risk elephant–railway corridors. The key operational challenges included delayed detection of elephant presence, limited night-time visibility, dependence on manual patrolling, and fragmented coordination between Forest and Railway authorities.
The AI-enabled system has shifted the approach from reactive response to proactive prevention. Thermal and optical sensors with real-time analytics now enable early detection and instant alerts to railway personnel. With over 6,000 safe elephant crossings facilitated and more than 250 high-risk alerts enabling timely train slowdowns, elephant–train collisions have been significantly reduced in monitored stretches.
The intervention demonstrates how technology can enhance frontline capacity and institutional coordination.
The AI and ML-based elephant movement monitoring system along railway tracks has drawn national attention. Could you explain how real-time data and predictive alerts are integrated into decision-making for forest and railway authorities?
The system uses thermal and optical cameras equipped with AI-based object detection to identify elephants near railway tracks in real time.
Once a detection crosses a predefined risk threshold, alerts are transmitted instantly to station masters, railway control rooms, and loco pilots. Operational decisions such as speed reduction are taken within seconds. Simultaneously, data is logged in a centralised monitoring system for analytics, performance evaluation, and future model refinement.
The architecture ensures low-latency communication, seamless integration with railway operations, and continuous feedback for system improvement.
The AI-powered Command Centre in Gudalur is seen as a critical institutional innovation. How does this centre function as a coordination hub across departments and what role has technology played in reducing response time and wildlife fatalities?
The Command Centre functions as an integrated coordination hub, consolidating live AI camera feeds, alert validation systems, and interdepartmental communication channels between the Forest and Railway authorities.
Automated detection and threshold-based alert triggering reduce manual dependency and response delays. The Centre enables rapid communication, structured documentation of events, and performance monitoring.
By institutionalising real-time decision support, response times have been reduced and wildlife fatalities in monitored areas have declined. It represents a shift toward data-driven, accountable conservation governance.
From a policy perspective, what were the key challenges during implementation, whether technological, institutional, or on the ground, and how were they addressed?
Technological challenges:
Ensuring high detection accuracy across varied terrain, vegetation density, fog, and low-light conditions; reducing false positives and false negatives.
Institutional challenges:
Coordinating seamless communication between Forest and Railway authorities; defining clear operational protocols.
Operational challenges:
Building confidence among field staff; ensuring compliance with rapid response protocols.
These were addressed through pilot deployment, model retraining using local datasets, clear Standard Operating Procedures (SOPs), joint coordination mechanisms, and continuous field-level training and system calibration.
What best practices have emerged from Tamil Nadu’s experience that other states can realistically adopt, especially those facing similar human-animal conflict in forest-fringe and rail-adjacent areas?
Key best practices include:
- Modular and scalable deployment beginning with high-risk corridors
- Integration of AI alerts into existing railway communication systems
- Low-latency, real-time alert architecture
- Continuous model retraining using real-world field data
- Strong interdepartmental coordination protocols
These practices make the system practical, cost-effective, and replicable in other forest-fringe and rail-adjacent landscapes.
Data governance and ethical use of AI are growing concerns. How does the department ensure responsible use of AI, accuracy of alerts and community trust in these technology-driven interventions?
The system is designed exclusively for wildlife detection and not for human surveillance. Data is securely stored, access-controlled, and used strictly for conservation and safety purposes.
Accuracy is maintained through threshold-based alerting, thermal-visual cross-verification, periodic model audits, and continuous recalibration. Transparent communication with railway staff and stakeholders strengthens trust and ensures accountability.
Responsible AI deployment is embedded within operational protocols and oversight mechanisms.
Looking ahead, how does the department plan to scale or deepen the use of advanced technologies such as AI, drones, or satellite-based monitoring, for wildlife conservation and climate resilience?
The forward pipeline includes:
- Expansion of AI-based monitoring to additional wildlife corridors
- Integration of drone-based surveillance for wider spatial coverage
- Use of satellite imagery and GIS analytics for migration mapping
- Development of predictive risk models using seasonal and movement data
- Creation of an integrated Wildlife Intelligence Platform combining AI, geospatial tools, and climate analytics
The objective is to build a comprehensive, technology-enabled environmental governance framework.
Finally, how do you see technology-enabled environmental governance shaping India’s broader conservation strategy and what role can states play in setting national benchmarks?
Technology-enabled environmental governance enables evidence-based decision-making, real-time risk mitigation, measurable outcomes, and improved transparency.
States can function as innovation laboratories, piloting scalable solutions that inform national policy. Tamil Nadu’s model demonstrates how technology can harmonise infrastructure development with ecological protection.
By institutionalising data-driven conservation, states can help set national benchmarks for responsible, technology-enabled environmental governance.
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