IoT Based Anti-Poaching System for Trees and Wildlife Monitoring System in Remote Area

Abstract

Illegal poaching and deforestation pose significant threats to biodiversity, leading to the decline of wildlife populations and environmental degradation. To address this issue, we propose an IoT-based Anti-Poaching System for real-time monitoring and activity detection in remote forest areas. The system leverages a Raspberry Pi as a low-power, cost-effective device for video streaming and uploading footage to cloud storage for further analysis. For animal detection, a machine learning-based model is employed to recognize species and track their movements. This enables wildlife conservationists to monitor animals' presence in different areas and detect any anomalies. Additionally, a YOLO (You Only Look Once) deep learning model is integrated for fire detection, allowing early identification of wildfires caused by illegal activities such as poaching camps or deforestation. The captured data is processed in real-time, and any detected suspicious activities, such as unauthorized human presence, gunshots (via sound recognition), or fires, trigger automated alerts. These alerts are sent to forest authorities via a mobile application or web-based dashboard, enabling rapid intervention. The system also supports GPS tracking for pinpointing the exact location of detected threats. By combining IoT, deep learning, and real-time analytics, this smart surveillance system enhances forest conservation efforts by providing automated monitoring, early threat detection, and rapid response capabilities. The proposed solution aims to minimize illegal poaching activities and protect endangered wildlife, ensuring a sustainable and secure ecosystem.