Leveraging Machine Learning and Raspberry Pi for Enhanced Wildlife Remote Monitoring and Localization

• Published in: 2023 International Conference on Modeling & E-Information Research, Artificial Learning and Digital Applications (ICMERALDA) pp. 301 - 306
• Main Authors: Manzi, Fabrice, Tuyishime, Emmanuel, Hitayezu, Antoine, Muhawenayo, Gedeon, Nsengiyumva, Philibert, Jayavel, Kayalvizhi
• Format: Conference Proceeding
• Language: English
• Published: IEEE 24.11.2023
• Subjects: Cameras; Embedded Computing; Handover; Location awareness; Machine learning; Object Detection; Raspberry Pi; Sociology; Wildlife; YOLO; YOLOv5
• DOI: 10.1109/ICMERALDA60125.2023.10458213

The tourism sector faces challenges, particularly concerning the expectations of wildlife enthusiasts visiting the national parks. The occasional difficulty in spotting favored wildlife species can lead to a dissonance between anticipated and actual experiences, potentially impacting visitors' overall park experience. To address this issue, we propose a novel system integrating the You Only Look Once (YOLOv5) machine learning model with a Raspberry Pi and an intuitive client application to enrich tourists' experience. This study presents the outcomes of our proposed system, showcasing its potential to assist tourists in wildlife localization and effective trip planning. Beyond enhancing tourist encounters, this system holds promise in the domains of wildlife conservation and animal behavior research, furnishing an advanced tool for monitoring and comprehending animal populations in their natural habitats, and contributing significantly to conservation initiatives. While this study presents a single system within a specific location, the scalability of this approach across entire national parks is evident.