Proceedings of the International Conference on Challenges and Trends in Arts and Social Sciences (ICCTASS 2025)

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AI‑Driven Framework for Cattle Breed Identification to Support Sustainable Livestock and One Health

Authors
Shourav Dey1, *, Aishwarya Debnath Ayshi2, Arthy Roy Chowdhury3
1Department of Information and Communication Engineering, Noakhali Science and Technology University, Noakhali, Bangladesh
2Department of Information Technology, The Kyoto College of Graduate Studies for Informatics, Kyoto, Japan
3Department of Business Administration, Noakhali Science and Technology University, Noakhali, Bangladesh
*Corresponding author. Email: shourav1115@student.nstu.edu.bd
Corresponding Author
Shourav Dey
Available Online 30 May 2026.
DOI
10.2991/978-2-38476-581-2_20How to use a DOI?
Keywords
Public Health; Sustainable Livestock; One Health; Breed Detection; Transfer Learning
Abstract

In developing nations, livestock has a significant impact on public health and food security, making them a critical source of income, nutrition, and agricultural resilience. The identification of breeds accurately remains a challenge for small-scale producers, resulting in suboptimal nutrition, inadequate disease control, and reduced sustainability. This research establishes an effective framework for cow breed detection via transfer learning, assessed on an enhanced dataset of 6,040 photos spanning five breeds. A comparison was conducted between four architectures: LaVin-DiT, CoAtNet, EfficientNetV2, and lightweight DAMambaNet. EfficientNetV2 attained exceptional accuracy (95.6%), but DAMambaNet provided practical benefits (6.18 MB, 0.5 ms inference) for mobile deployment in rural areas. Advancing One Health objectives, the framework facilitates precise breed identification to inform reproduction, vaccination, and nutritional decisions. Field validation continues to be indispensable for verifying the agronomic advantages.

Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Challenges and Trends in Arts and Social Sciences (ICCTASS 2025)
Series
Advances in Social Science, Education and Humanities Research
Publication Date
30 May 2026
ISBN
978-2-38476-581-2
ISSN
2352-5398
DOI
10.2991/978-2-38476-581-2_20How to use a DOI?
Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Shourav Dey
AU  - Aishwarya Debnath Ayshi
AU  - Arthy Roy Chowdhury
PY  - 2026
DA  - 2026/05/30
TI  - AI‑Driven Framework for Cattle Breed Identification to Support Sustainable Livestock and One Health
BT  - Proceedings of the International Conference on Challenges and Trends in Arts and Social Sciences (ICCTASS 2025)
PB  - Atlantis Press
SP  - 258
EP  - 269
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-581-2_20
DO  - 10.2991/978-2-38476-581-2_20
ID  - Dey2026
ER  -