Bird Species Recognition Using YOLOv8: A Deep Learning Approach for Habitat Conservation and Preservation

Accurate identification of bird species is essential for conservation and ecological studies. This study proposed a real-time bird detection system using an advanced deep learning model, YOLOv8, developed to perform well against ecological obstacles. The model was trained using a custom dataset, where the data was made and labeled through Roboflow, at a resolution of 640 × 640 pixels over 50 epochs. The model exhibited an inference speed of 2.9 milliseconds per image, and offered a mean Average Precision (mAP 50) of 94.3%, suggesting accuracy and efficiency in comparison to earlier models like YOLOv5 and Faster R-CNN. Performance recognition against the model was completed with several bird species including the Jawa Sparrow, Nicobar Pigeon, and Golden Eagle, which suggested reliability against with lower ambient light and cluttered backgrounds. The model can also be deployed on edge devices such as the NVIDIA Jetson Nano, which also suggested usability for large size ecological monitoring. Providing real-time knowledge of species distribution, population trends, and habitat change monitoring, makes this solution viable for conservationists and wildlife researchers, resulting in sound data driven environmental decision making. Keywords: Bird species identification; YOLOv8; Object detection; biodiversity.