Wildlife populations worldwide face increasing threats from habitat loss, poaching, and climate change. This review examines the application of Artificial Intelligence and Computer Vision techniques for wildlife animal detection and species recognition. Seven key studies published between 2020 and 2026 are analysed, covering CNN-based detection, YOLO architectures, transfer learning, bioacoustic monitoring, and IoT-integrated alert systems. Findings indicate that YOLO-based models consistently achieve above 90% mAP, while lightweight variants enable edge deployment. Future directions include multi-modal fusion and individual recognition at scale.
Kale et al. (Fri,) studied this question.