Review on methods used for wildlife species and individual identification

This work presented a literature review on animal species and individual identification tools, as well as animal monitoring capabilities. We gathered the literature to cover different aspects of technologies that are widely in use for animal identification, from the traditional up to the latest methods. This study includes species and individual animal identification attributes namely body patterns, footprints, facial features, and sound for identification purposes. The large volume of data collected could be automatically processed using machine learning and deep learning techniques to achieve both species and individual animal identification more efficiently as compared to the human workforce. It is a much faster and accurate approach considering the large volume of data, than manual processing, which is extremely expensive, time-consuming, tedious, and monotonous. We established that machine learning and advancements in deep learning hold significant promise to high-accuracy identification of both species and individual animal. Methods used for individual identification are mainly implemented in endangered species by the conservation management. The traditional methods such as the use of footprints, drawings of animal biometrics are integrated into the recent growth of technology to eliminate the human skill needed to achieve species and individual identification through the use of machine learning and deep learning algorithms for automatic identification purposes.