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2023Conference PaperSpringer4 citations

ECO-Guard: An Integrated AI Sensor System for Monitoring Wildlife and Sustainable Forest Management

Ch Nikhilesh Krishna, Avishek Rauniyar, N Kireeti Sai Bharadwaj, Sujay Bharath Raj, Vipina Valsan, Kavya Suresh, V Ravikumar Pandi, Soumya Sathyan

International Conference on Information and Communication Technology for Smart Systems (ICTCS 2023)

Overview

Abstract

Forest ecosystems face increasing threats from climate change, illegal logging, and human encroachment. Traditional wildlife monitoring methods are labor-intensive, expensive, and fail to provide real-time insights for conservation decision-making. ECO-Guard presents an integrated AI sensor system that combines computer vision, IoT sensor networks, and edge computing for continuous wildlife monitoring and sustainable forest management. The system deploys camera traps with on-device ML inference for species detection, environmental sensors for habitat monitoring, and a centralized dashboard for conservation analytics.

Keywords
AIComputer VisionIoTWildlife MonitoringSmart Forest SystemsEdge ComputingConservation Technology
Research Areas
AIComputer VisionIoTWildlife MonitoringSmart Forest Systems

Technical Details

Technologies Used
PythonTensorFlowComputer VisionIoT SensorsEdge ComputingRaspberry Pi
Datasets
  • Wildlife camera trap dataset
  • Environmental sensor readings
Models
  • CNN-based species classifier

My Contribution

Role

Co-author — AI system architecture, computer vision pipeline, IoT framework integration, field testing.

Details

Co-author. Contributed to the AI sensor system architecture, computer vision pipeline for wildlife monitoring, and IoT data collection framework integration. Participated in field testing and paper writing.

Challenges

Limited training data for rare wildlife species in the target ecosystem
Power and connectivity constraints in remote forest deployment locations
Balancing on-device inference accuracy with edge device compute limitations

Key Contributions

Integrated AI + IoT architecture for continuous wildlife monitoring
On-device ML inference pipeline optimized for edge deployment
Real-time conservation dashboard for forest management authorities

Impact

Demonstrated a scalable approach to automated wildlife monitoring that reduces manual survey costs while increasing temporal coverage. Applicable to conservation efforts globally.

Lessons Learned

Edge deployment requires different optimization strategies than cloud — model compression was critical
Sensor reliability in outdoor conditions is the hardest part of IoT AI systems
Conservation stakeholders need actionable dashboards, not raw ML metrics

Citation

APA

Krishna, C. N., Rauniyar, A., Bharadwaj, N. K. S., Raj, S. B., Valsan, V., Suresh, K., Pandi, V. R., & Sathyan, S. (2023). ECO-Guard: An integrated AI sensor system for monitoring wildlife and sustainable forest management. In International Conference on Information and Communication Technology for Smart Systems (ICTCS 2023). Springer. https://doi.org/10.1007/978-981-99-9489-2_36

IEEE

C. N. Krishna, A. Rauniyar, N. K. S. Bharadwaj, S. B. Raj, V. Valsan, K. Suresh, V. R. Pandi, and S. Sathyan, "ECO-Guard: An Integrated AI Sensor System for Monitoring Wildlife and Sustainable Forest Management," in Proc. Int. Conf. Inf. Commun. Technol. Smart Syst. (ICTCS), 2023, pp. 409–419.

MLA

Krishna, Ch Nikhilesh, et al. "ECO-Guard: An Integrated AI Sensor System for Monitoring Wildlife and Sustainable Forest Management." International Conference on Information and Communication Technology for Smart Systems (ICTCS 2023), Springer, 2023, pp. 409-419.

Chicago

Krishna, Ch Nikhilesh, Avishek Rauniyar, N. Kireeti Sai Bharadwaj, Sujay Bharath Raj, Vipina Valsan, Kavya Suresh, V. Ravikumar Pandi, and Soumya Sathyan. "ECO-Guard: An Integrated AI Sensor System for Monitoring Wildlife and Sustainable Forest Management." In International Conference on Information and Communication Technology for Smart Systems (ICTCS 2023), 409–19. Springer, 2023. https://doi.org/10.1007/978-981-99-9489-2_36.

BibTeX
@inproceedings{krishna2023ecoguard,
  title     = {ECO-Guard: An Integrated AI Sensor System for Monitoring Wildlife and Sustainable Forest Management},
  author    = {Krishna, Ch Nikhilesh and Rauniyar, Avishek and Bharadwaj, N Kireeti Sai and Raj, Sujay Bharath and Valsan, Vipina and Suresh, Kavya and Pandi, V Ravikumar and Sathyan, Soumya},
  booktitle = {International Conference on Information and Communication Technology for Smart Systems (ICTCS 2023)},
  year      = {2023},
  publisher = {Springer},
  doi       = {10.1007/978-981-99-9489-2_36}
}