AI and IoT Based Anti-Poaching System
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Abstract
Poaching of wildlife and deforestation are constant threats to ecological balance and biodiversity across the globe, particularly in nations such as India with extensive forest cover and diverse fauna richness. Conventional manual patrolling is limited in scale, coverage, and speed, making it impossible to prevent wildlife offences in remote forest areas. This paper presents the design and proposed deployment of an IoT- and AIenabled Anti-Poaching System for real-time detection of human intrusion and gunfire in safeguarded forest reserves. The solution includes an array of multi-sensor pods with cameras, microphones, and GPS modules. Lightweight AI models analyse sensed information, developed and evaluated on benchmark datasets, for human and animal identification, sound categorisation, and prompt alert generation. The AI system achieves high detection accuracy and low inference latency, as demonstrated by experimental evaluation, making it feasible for future integration into IoT hardware. This work highlights the value of integrating embedded systems, AI, and IoT technologies to develop cost-effective, scalable, and energy-efficient antipoaching solutions tailored to remote, resource-constrained forest environments.
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Lavadinović, V. M., Islas, C. A., Chatakonda, M. K., Marković, N., & Mbiba, M. (2021). Mapping the research landscape on poaching: A decadal systematic review. Frontiers in Ecology and Evolution, 9, Article 630990. DOI: https://doi.org/10.3389/fevo.2021.630990
Sharma, S., Sato, K., & Gautam, B. P. (2023). A methodological literature review of acoustic wildlife monitoring using artificial intelligence tools and techniques. Sustainability, 15(9), 7128. DOI: https://doi.org/10.3390/su15097128
Kamminga, J., Ayele, E., Meratnia, N., & Havinga, P. (2018). Poaching detection technologies—A survey. Sensors, 18(5), 1474.
DOI: https://doi.org/10.3390/s18051474
Anand, S., & Radhakrishna, S. (2017). Investigating trends in human-wildlife conflict: Is conflict escalation real or imagined? Journal of Asia-Pacific Biodiversity, 10(2), 154–161. DOI: https://doi.org/10.1016/j.japb.2017.02.003
Paul, J. K., Yuvaraj, T., & Gundepudi, K. (2020). Demonstrating a low-cost unmanned aerial vehicle for anti-poaching. In 2020, IEEE 17th India Council International Conference (INDICON) (pp. 1–7). IEEE. DOI: https://doi.org/10.1109/INDICON49873.2020.9342131
Banzi, J. F. (2014). A sensor-based anti- poaching system in Tanzania national parks. International Journal of Scientific and Research Publications, 4(4), 1–7. https://www.ijsrp.org/research-paper-0414/ijsrp-p2815.pdf
Tan, T. F., Teoh, S. S., Fow, J. E., & Yen, K. S. (2016). An embedded human detection system based on thermal and infrared sensors for an anti-poaching application. In 2016 IEEE Conference on Systems, Process and Control (ICSPC) (pp. 37– 42). IEEE. DOI: https://doi.org/10.1109/SPC.2016.7920700
Khallikkunaisa, Y. A., V. V., Y. H. S., & Y. K. S. (2024). An anti-poaching system for protecting forests and wildlife using IoT and ZigBee technology. In 2024 International Conference on Distributed Computing and Optimization Techniques (ICDCOT) (pp. 1–7). IEEE.
DOI: https://doi.org/10.1109/ICDCOT61034.2024.10516085
Ishitha, S., Nagaraju, S., Mohan, H. A., Harshitha, M., Gowda, G. R., & J. N. (2021). IoT-based anti-poaching and fire alarm system for forests. In 2021, IEEE Mysore Sub Section International Conference (MysuruCon) (pp. 711–715). IEEE.
DOI: https://doi.org/10.1109/MysuruCon52639.2021.9641539
Neetu, D., Sreedhar, R., Sreekumar, R., T., & P. J. (2024). Forest intrusion detection and anti- poaching alarm system. In 2024 International Conference on Smart Systems for Electrical, Electronics, Communication and Computer Engineering (ICSSEECC) (pp. 366–368). IEEE.
DOI: https://doi.org/10.1109/ICSSEECC61126.2024.10649453
Rana, A. K., & Kumar, N. (2023). Current wildlife crime (Indian scenario): Major challenges and prevention approaches. Biodiversity and Conservation, 32(5), 1473–1491. DOI: https://doi.org/10.1007/s10531-023-02577-z
COCO Dataset. [Online]. Available: https://cocodataset.org/
Kaggle Gunshot Audio Dataset. [Online]. Available: https://www.kaggle.com/datasets
RunPod Cloud GPU Service. [Online]. Available: https://www.runpod.io
OpenCV Library. [Online]. Available: https://opencv.org/
PyTorch: An open-source machine learning framework. [Online]. Available: https://pytorch.org/
Librosa: Audio and Music Signal Processing in Python. [Online]. Available: https://librosa.org/
Pushbullet Python Library. [Online]. Available: https://github.com/randomchars/pushbullet.py
Arduino Documentation. [Online]. Available: https://www.arduino.cc/