Đề án tốt nghiệp Thạc sĩ: Xây dựng và phát triển hệ thống trí tuệ nhân tạo cho giám sát và cảnh báo trong khu rừng bảo tồn

The degradation of forest ecosystems and biodiversity in Vietnam, exacerbated by illegal logging, forest fires, and wildlife trafficking, presents a critical challenge to national conservation efforts. Traditional monitoring methods, often labor-intensive and lacking modern technological support, have proven insufficient. In this context, the integration of artificial intelligence (AI) offers a transformative solution for intelligent surveillance within conservation areas. This research addresses the urgent need for advanced systems that can automatically detect human intrusion and wildlife, providing timely warnings to safeguard natural resources. The aim is to design, develop, and implement an effective AI-driven monitoring and warning system tailored for Vietnam's conservation forests.

This academic content is primarily intended for researchers, environmental scientists, conservation practitioners, policymakers in natural resource management, and students or professionals in artificial intelligence, machine learning, and information systems interested in ecological applications.

This study addresses the escalating issues of forest degradation and biodiversity loss in Vietnam by proposing an innovative artificial intelligence (AI) system for monitoring and early warning in conservation areas. Recognizing the limitations of traditional surveillance, the research develops an integrated solution capable of automating the detection of human activities, such as illegal logging and poaching, and the identification of wildlife species. The core logic involves leveraging advanced AI models, particularly deep learning architectures like Convolutional Neural Networks (CNNs) and You Only Look Once (YOLO), known for their high accuracy and real-time processing capabilities in object detection. The system integrates various hardware components, including infrared cameras, motion sensors (PIR), GPS modules, and low-power microcontrollers such as Raspberry Pi, designed for robust operation in challenging forest environments with limited network infrastructure. The methodology encompasses a comprehensive approach, combining theoretical analysis of existing monitoring systems and AI algorithms with practical experimental deployment in real conservation settings. Performance evaluation focuses on metrics such as animal identification accuracy, intrusion detection rates, and system response times, with insights gained informing future optimizations. This AI-driven system not only enhances the efficiency and effectiveness of forest protection but also provides valuable data for scientific research, informed policy-making in biodiversity conservation, and environmental education. Its application value lies in its potential to reduce illegal activities, support wildlife research, and promote broader community awareness regarding the critical importance of preserving Vietnam's natural heritage. The developed model is designed for scalability, allowing its implementation across diverse national conservation areas.