Volume 44 Issue 6
Nov.  2023
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Yu-Juan Zhang, Zeyu Luo, Yawen Sun, Junhao Liu, Zongqing Chen. From beasts to bytes: Revolutionizing zoological research with artificial intelligence. Zoological Research, 2023, 44(6): 1115-1131. doi: 10.24272/j.issn.2095-8137.2023.263
Citation: Yu-Juan Zhang, Zeyu Luo, Yawen Sun, Junhao Liu, Zongqing Chen. From beasts to bytes: Revolutionizing zoological research with artificial intelligence. Zoological Research, 2023, 44(6): 1115-1131. doi: 10.24272/j.issn.2095-8137.2023.263

From beasts to bytes: Revolutionizing zoological research with artificial intelligence

doi: 10.24272/j.issn.2095-8137.2023.263
Supplementary data to this article can be found online.
The authors declare that they have no competing interests.
Y.J.Z. and Z.C. conceived the review. Y.J.Z., Y.S., J.L., Z.L, and Z.C. prepared the manuscript, figures, and tables. All authors read and approved the final version of the manuscript.
Funds:  This work was supported by the National Natural Science Foundation of China (31871274), Natural Science Foundation of Chongqing, China (CSTB2022NSCQ-MSX0650), Science and Technology Research Program of Chongqing Municipal Education Commission (KJQN202100508), Team Project of Innovation Leading Talent in Chongqing (CQYC20210309536), and “Contract System” Project of Chongqing Talent Plan (cstc2022ycjh-bgzxm0147)
More Information
  • Corresponding author: E-mail: chern@cqnu.edu.cn
  • Received Date: 2023-08-21
  • Accepted Date: 2023-10-30
  • Published Online: 2023-10-31
  • Publish Date: 2023-11-18
  • Since the late 2010s, Artificial Intelligence (AI) including machine learning, boosted through deep learning, has boomed as a vital tool to leverage computer vision, natural language processing and speech recognition in revolutionizing zoological research. This review provides an overview of the primary tasks, core models, datasets, and applications of AI in zoological research, including animal classification, resource conservation, behavior, development, genetics and evolution, breeding and health, disease models, and paleontology. Additionally, we explore the challenges and future directions of integrating AI into this field. Based on numerous case studies, this review outlines various avenues for incorporating AI into zoological research and underscores its potential to enhance our understanding of the intricate relationships that exist within the animal kingdom. As we build a bridge between beast and byte realms, this review serves as a resource for envisioning novel AI applications in zoological research that have not yet been explored.
  • Supplementary data to this article can be found online.
    The authors declare that they have no competing interests.
    Y.J.Z. and Z.C. conceived the review. Y.J.Z., Y.S., J.L., Z.L, and Z.C. prepared the manuscript, figures, and tables. All authors read and approved the final version of the manuscript.
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