A FOREST MONITORING SYSTEM BASED ON GRPS AND POWERED BY IOT

Abstract

Forests are increasingly threatened by deforestation, climate change, and natural hazards such as wildfires and rising atmospheric pollution. Addressing these challenges requires efficient and real-time monitoring systems. This study presents an intelligent forest monitoring framework built on the Raspberry Pi Model 3, integrating both analog and digital sensors with advanced signal processing techniques. The system continuously measures key environmental parameters, including temperature, gas levels, and soil moisture, to assess forest conditions. In addition to environmental sensing, the proposed framework incorporates an audio classification module that analyzes ambient sounds using machine learning algorithms. Detected sounds are categorized into distinct classes such as chainsaw activity, vehicle movement, or natural forest noise, enabling early identification of illegal logging or human intrusion. The collected data is transmitted through an Internet-based platform and made accessible via mobile applications, ensuring real-time user interaction. Alerts and notifications are generated automatically when abnormal conditions— such as fire risks, elevated pollution levels, or unauthorized deforestation activities are detected. This IoT-based solution is designed to support forest management efforts by providing timely insights to government agencies, environmental organizations, and private stakeholders, thereby enhancing conservation strategies and disaster response capabilities.