Combatting Illegal Logging with AI-powered IoT Devices for Forest Monitoring

Authors

  • Abdullah Khan University of Engineering & Technology, Peshawar, Pakistan
  • Hamza Ali Computer Systems Engineering University of Engineering and Technology, Peshawar, Pakistan
  • Maham Jadoon Computer Systems Engineering University of Engineering and Technology, Peshawar, Pakistan
  • Zain Ul Abideen Computer Systems Engineering University of Engineering and Technology, Peshawar, Pakistan
  • Nasru Minuallah Computer Systems Engineering University of Engineering and Technology, Peshawar, Pakistan

Keywords:

Forest Monitoring, AI Against Illegal Logging, Real-time Alerts, Environmental Conservation, IoT for Anti- Logging

Abstract

This research presents a comprehensive strategy for tackling illegal logging by leveraging Artificial Intelligence (AI) and Internet of Things (IoT) technologies. In high-risk forestry areas, sensors-equipped Internet of Things devices are used to continuously monitor and detect the sound of the surroundings. The AI component uses machine learning methods to identify potential unlawful logging activities by accurately detecting and distinguishing sound patterns associated with chainsaw and logging operations such as tree cutting and also detecting natural disasters like wildfires. When such activities are detected by these smart AI-powered IoT devices installed in the forest, real-time notifications are generated after such activity which allows surrounding enforcement agencies, such as the forest department, to intervene promptly. By providing a targeted and prompt solution to the issue of illicit logging, this strategy supports biodiversity preservation and sustainable forest management.

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Published

2024-05-22

How to Cite

Khan, A., Ali, H., Jadoon, M., Abideen, Z. U., & Nasru Minuallah. (2024). Combatting Illegal Logging with AI-powered IoT Devices for Forest Monitoring. International Journal of Innovations in Science & Technology, 6(5), 134–142. Retrieved from https://journal.50sea.com/index.php/IJIST/article/view/792

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