Artificial Intelligence in Taxonomy: Advancing Species Identification and Classification

Abstract

Taxonomy constitutes a fundamental aspect of the study of biological diversity, conservation, and ecological and evolutionary research. Nevertheless, traditional taxonomic methodologies, which depend on morphological and molecular analyses, are frequently slow, necessitate considerable expertise, and are inefficient in keeping pace with the rapid expansion of biological data. In recent years, Artificial Intelligence (AI) has emerged as a potent solution to these limitations, facilitating the automatic, accurate, and scalable identification and classification of species. This paper reviews the application of AI in taxonomy, with an emphasis on image, DNA, and acoustic species identification. It examines machine learning and deep learning algorithms, such as convolutional neural networks, recurrent neural networks, and multimodal learning systems, in comparison to traditional taxonomic practices. To underscore the efficiency of AI models relative to human specialists, performance evaluation metrics, including accuracy, precision, recall, and F1-score, are discussed. Additionally, the paper addresses critical issues such as data imbalance, limited model interpretability, ethical considerations, and the reliance on high-quality labeled data. Finally, future research directions are outlined, including explainable AI, integrative taxonomy, citizen science engagement, and standardized benchmarks. Overall, the research demonstrates that AI-based taxonomy, when employed as an auxiliary tool alongside human expertise, can significantly expedite species discovery, monitor biodiversity, and contribute to global conservation efforts.