Abstract: Wildlife poaching poses a severe threat to biodiversity, demanding advanced prevention strategies. This paper investigates real-time monitoring technologies to enhance wildlife protection. By integrating satellite imaging, unmanned aerial vehicles (UAVs), and ground-based sensors, conservationists can achieve comprehensive surveillance over remote areas. Satellite imaging offers macro-level data on habitat changes and potential poaching activities. UAVs, with high-resolution cameras and thermal imaging, provide detailed, on-demand monitoring and rapid response capabilities. Ground-based sensors, such as motion detectors and acoustic sensors, ensure continuous, localized surveillance, alerting rangers to unauthorized human presence. Advanced data analytics and artificial intelligence synthesize these technologies, enabling pattern detection and prediction of poaching hotspots. This integrated approach enhances situational awareness and optimizes resource allocation for patrols. Case studies from African and Asian reserves demonstrate the success of these technologies in reducing poaching incidents. The paper concludes with a discussion on challenges and future directions, emphasizing sustainable and scalable solutions.

Keywords: Convolutional Neural Networks(CNN), Acoustic detection, Edge AI, Machine learning, Deep learning, Trail Guard AI.

| DOI: 10.17148/IARJSET.2024.111110