From Bioacoustics to Artificial Intelligence: A Bibliometric Study of Stress and Welfare Monitoring in Poultry: From Bioacoustics to Artificial Intelligence: A Bibliometric Study

Berkay ŞAHNAR; Cem TIRINK

doi:10.5281/zenodo.23142042

Authors

Berkay ŞAHNAR Igdir University, Faculty of Agriculture, Department of Animal Science, TR76000, Igdir, Turkey
Cem TIRINK Igdir University, Faculty of Agriculture, Department of Animal Science, TR76000, Igdir, Turkey https://orcid.org/0000-0001-6902-5837

DOI:

https://doi.org/10.5281/zenodo.23142042

Abstract

In this study, the scientific literature regarding the acoustic monitoring of stress and welfare in poultry was examined from both bibliometric and thematic perspectives. Publications covering the period from 1987 to 2025 were retrieved from the Web of Science Core Collection database; only research articles and reviews in English were included in the evaluation. Bibliometric analyses were conducted using the Bibliometrix package and the Biblioshiny interface within the R environment. Annual scientific output, sources, authors, countries, citation structure, keyword co-occurrence networks, and thematic structure were analyzed. The findings revealed significant growth in the research field, particularly after 2020, with an annual scientific output growth rate of 8.47%. While “Poultry Science”, “Applied Animal Behaviour Science”, and “Animals” emerged as the most productive publication sources, keyword and thematic analyses highlighted concepts such as animal welfare, behaviour, poultry, broiler, bioacoustics, precision livestock farming, and machine learning. Notably, the inclusion of "poultry–machine learning" and "animal welfare–behaviour" clusters among the motor themes indicates an evolution in the field from classical bioacoustics approaches toward AI-supported, automated welfare monitoring systems. In conclusion, the study finds that research on acoustic monitoring in poultry is becoming increasingly interdisciplinary and that AI-based methods could play a pivotal role in the future development of objective, continuous, and non-invasive welfare assessment systems.

References

Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007

Aria, M., Cuccurullo, C., D'Aniello, L., & Spano, M. (2026), Biblioshiny and the SAAS Workflow: An integrated framework for transparent and reproducible science mapping. A demonstration through the replication of a study, Journal of Informetrics, DOI: 10.1016/j.joi.2026.101837.

Aria, M., & Cuccurullo, C. (2026), Science Mapping Analysis - A primer with Biblioshiny, McGraw-Hill, ISBN: 978-88-386-2297-7.

Coutant, M. P., Villain, A. S., & Briefer, E. F. (2024). A scoping review of the use of bioacoustics to assess various components of farm animal welfare. Applied Animal Behaviour Science, 275, 106286. doi:10.1016/j.applanim.2024.106286.

Laurijs, K. A., Briefer, E. F., Reimert, I., & Webb, L. E. (2021). Vocalisations in farm animals: A step towards positive welfare assessment. Applied Animal Behaviour Science, 236, 105264. doi:10.1016/j.applanim.2021.105264.

Lev-Ron, T., Yitzhaky, Y., Halachmi, I., & Druyan, S. (2025). Classifying vocal responses of broilers to environmental stressors via artificial neural network. Animal, 19(1), 101. doi:10.1016/j.animal.2024.101378.

Manikandan, V., & Neethirajan, S. (2025a). AI-Powered Vocalization Analysis in Poultry: Systematic Review of Health, Behavior, and Welfare Monitoring. Sensors (Basel, Switzerland), 25(13), 4058. doi:10.3390/s25134058.

Manikandan, V., & Neethirajan, S. (2025b). Decoding poultry welfare from sound—a machine learning framework for non-invasive acoustic monitoring. Sensors, 25(9), 2912.

McLoughlin, M. P., Stewart, R., & McElligott, A. G. (2019). Automated bioacoustics: Methods in ecology and conservation and their potential for animal welfare monitoring. Journal of the Royal Society Interface, 16(155), 20190225. doi:10.1098/rsif.2019.0225.

Naeem, M., Jia, Z., Wang, J., Poudel, S., Manjankattil, S., Adhikari, Y., ... & Bourassa, D. (2025). Advancements in machine learning applications in poultry farming: a literature review. Journal of Applied Poultry Research, 100602.

Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., ... Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71

Posit Software, PBC. (2026). RStudio: Integrated development environment for R. Posit Software, PBC, Boston, MA.

R Core Team (2026). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.

Soster, P. D. C., Grzywalski, T., Hou, Y., Thomas, P., Dedeurwaerder, A., De Gussem, M., Tuyttens, F., Devos, P., Botteldooren, D., & Antonissen, G. (2025a). Automated detection of broiler vocalizations: A machine learning approach for broiler chicken vocalization monitoring. Poultry Science, 104(5), 104962. doi:10.1016/j.psj.2025.104962.

Soster, P., Devos, P., Smets, R., Tuyttens, F. A., Andretta, I., Buyse, K., De Gussem, M., & Antonissen, G. (2025b). A systematic review linking welfare and automated analysis of acoustic signals in broiler chickens and layers. World's Poultry Science Journal, 81(4), 1049-1080. doi:10.1080/00439339.2025.2543042.

From Bioacoustics to Artificial Intelligence: A Bibliometric Study of Stress and Welfare Monitoring in Poultry