In rural areas such as national parks, forests, and mountains, recognizing and classifying wildlife is crucial for monitoring animals that may pose risks to crops and human safety. Deep learning provides the most accurate approach due to its dynamic adaptability. However, it is also highly energy intensive. Given the constraints of rural environments, including the use of extreme edge devices and limited power availability, this study employs distributed computing to maximize battery life while enabling the execution of DL tasks. The core concept is Split Computing, specifically applied to the YOLOv8m and YOLOv10 models, which have been provided by researchers as a highly effective solution for wildlife classification. This approach dynamically reallocates different model components across available boards, specifically the NVIDIA Jetson Orin Nano, to optimize energy consumption. This research investigates various model distribution configurations across the head, backbone, and neck components, assessing whether video stream compression between the camera and the computing boards affects energy consumption and network load. Our findings suggest that the choice of model splitting configuration significantly impacts energy efficiency, bandwidth consumption, and computational load distribution.
