From beasts to bytes: Revolutionizing zoological research with artificial intelligence

Yu-Juan Zhang; Zeyu Luo; Yawen Sun; Junhao Liu; Zongqing Chen

doi:10.24272/j.issn.2095-8137.2023.263

Volume 44 Issue 6

Nov. 2023

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Article Navigation > Zoological Research > 2023 > 44(6): 1115-1131

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

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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

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From beasts to bytes: Revolutionizing zoological research with artificial intelligence

doi: 10.24272/j.issn.2095-8137.2023.263

1.
Chongqing Key Laboratory of Vector Insects; Chongqing Key Laboratory of Animal Biology; College of Life Science, Chongqing Normal University, Chongqing 401331, China
2.
School of Mathematical Sciences, Chongqing Normal University, Chongqing 401331, China
3.
Chongqing National Center for Applied Mathematics, Chongqing Normal University, Chongqing 401331, China

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

Abstract

Abstract

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.
- Animal science,
- Data extraction,
- Classification model,
- Behavior analysis,
- Biomolecular sequences analysis

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References(174)

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