Monitoring of cattle in confinement with the aid of drone, surveillance of welfare conditions and automated counting

Authors

DOI:

https://doi.org/10.33448/rsd-v14i2.47947

Keywords:

Drone; Aerial monitoring; Behavior; Animal health.

Abstract

The study aimed to evaluate the use of drones (UAS - Unmanned Aerial System) in monitoring confined cattle, with automated herd or individual cattle counting, focusing on disease detection and animal welfare monitoring. Flights were conducted using a DJI Mini 2 aerial device, operating at a minimum height of 5 meters over a cattle confinement area on a private property located in the municipality of Balsas, Maranhão. Observations were made for periods of 3 hours. The technology allowed for individual and group assessments of the animals, monitoring feeding areas, and checking for potential clinical signs of diseases. The unmanned aerial vehicle did not disturb the cattle, facilitating the detection of morbidity and mortality, while also reducing the need for labor. The Count Things from Photos app, integrated with the device, enabled the automated counting of cattle, emerging as an effective and low-cost technological innovation for sanitary surveillance and the welfare of confined cattle. The use of the unmanned aerial vehicle showed a high level of agreement with direct inspections and clinical examination of the animals and also demonstrated potential for monitoring cattle welfare from the confinement area to transport to the slaughterhouse, contributing to the traceability process of the animals. The app was effective in automated counting, whether individually or in groups of confined cattle.

References

Alocilla, O., & Monti, G. (2022). Network analysis of cattle movements in Chile: Implications for pathogen spread and control. Preventive Veterinary Medicine, 204, 105644. https://doi.org/10.1016/j.prevetmed.2022.105644

Arruda, M. F., Silva, R. G., & Oliveira, A. P. (2024). Vigilância clínica e espacial de bovinos em leilão com auxílio de drone e contagem automatizada por aplicativo. In Tecnologia e Produção Agropecuária (Cap. 3, pp. 50-144). https://doi.org/10.29327/5343106.1-3

Barcellos, J. O. J. (2016). Apontamentos estratégicos sobre a bovinocultura de corte brasileira. Archivos Latinoamericanos de Producción Animal, 24(3), 173-182.

Barbedo, J., Koenigkan, L., Santos, T., & Santos, P. (2019). A Study on the Detection of Cattle in UAV Images Using Deep Learning. Sensors (Basel, Switzerland), 19. https://doi.org/10.3390/s19245436.

Damiaans, B., Renault, V., Sarrazin, S., Berge, A. C., Pardon, B., Saegerman, C., & Dewulf, J. (2020). A risk-based scoring system to quantify biosecurity in cattle production. Preventive Veterinary Medicine, 179, 104992. https://doi.org/10.1016/j.prevetmed.2020.104992

Harras, Júlia Martins. O impacto da rastreabilidade animal na comercialização da carne bovina. 2023.

Heidmann, Maycon Junior; Do Nascimento, Cristiano Grisi; De Castro, Bruno Gomes. Complexo respiratório bovino no contexto da sanidade animal. Scientific Electronic Archives, v. 14, n. 4, 2021.

Herlin, A., Brunberg, E., Hultgren, J., Högberg, N., Rydberg, A., & Skarin, A. (2021). Animal Welfare Implications of Digital Tools for Monitoring and Management of Cattle and Sheep on Pasture. Animals: an Open Access Journal from MDPI, 11. https://doi.org/10.3390/ani11030829.

Instituto Brasileiro de Geografia e Estatística. (2023). Trimestrais da pecuária: Em 2023, abate de bovinos cresce e o de frangos e suínos atinge recordes. Agência de Notícias IBGE. https://www.ibge.gov.br/agencia-de-noticias

Instituto Brasileiro de Geografia e Estatística. (2024). Censo Agropecuário,2023. https://www.ibge.gov.br/estatisticas/economicas/agricultura-e-pecuaria/21814-2017-censo-agropecuario.html

Ijaz, M., Batool, A., Babar, M. E., Hayat, Z., & Waheed, U. (2020). Association between meat color of DFD beef and other quality attributes. Meat Science, 161, 107954. https://doi.org/10.1016/j.meatsci.2019.107954

Khanal, A. R., Gillespie, J. M., MacDonald, J. M., & Mathews, K. H. (2010). Adoption of technology, management practices, and production systems in US milk production. Journal of Dairy Science, 93(6), 6022-6010. https://doi.org/10.3168/jds.2010-3036

Koger, B., Jones, B., Tolkamp, B. J., & Ellwood, S. A. (2023). Quantifying the movement, behaviour and environmental context of group-living animals using drones and computer vision. Journal of Animal Ecology, 92(7), 1357-1371. https://doi.org/10.1111/1365-2656.14023

Mücher, C., Los, S., Franke, G., & Kamphuis, C. (2022). Detection, identification and posture recognition of cattle with satellites, aerial photography and UAVs using deep learning techniques. International Journal of Remote Sensing, 43, 2377 - 2392. https://doi.org/10.1080/01431161.2022.2051634.

Neethirajan, S. (2017). Recent advances in wearable sensors for animal health management. Sensing and Bio-Sensing Research, 15-29. https://doi.org/10.1016/j.sbsr.2017.03.003.

Pereira A. S. et al. (2018). Metodologia da pesquisa científica. UFSM.

Schirdewahn, F., Lentz, H. H. K., Colizza, V., Koher, A., Hövel, P., & Vidondo, B. (2021). Early warning of infectious disease outbreaks on cattle-transport networks. PLoS One, 16(1), e0244999. https://doi.org/10.1371/journal.pone.0244999

Taylor, E., Fisher, A. D., & McCarthy, M. (2024). Application of a welfare assessment protocol for Australian lot-fed cattle: The effect of time and frequency of assessment. Applied Animal Behaviour Science, 277, 106349. https://doi.org/10.1016/j.applanim.2023.106349

Welfare Quality Network. (2024). Assessment protocols. https://www.welfarequalitynetwork.net/en-us/reports/assessment-protocols

Downloads

Published

10/02/2025

How to Cite

ARRUDA, R. C. N. de .; PEDROSA , K. Y. F. .; SANTOS, M. A. G. de F. .; NEGREIROS, M. R. .; TORRES, M. A. O. .; NEGREIROS , I. R. .; LIMA, F. C.; SOARES, J. W. A. . Monitoring of cattle in confinement with the aid of drone, surveillance of welfare conditions and automated counting. Research, Society and Development, [S. l.], v. 14, n. 2, p. e3214247947, 2025. DOI: 10.33448/rsd-v14i2.47947. Disponível em: https://rsdjournal.org/index.php/rsd/article/view/47947. Acesso em: 2 apr. 2025.

Issue

Vol. 14 No. 2

Section

Agrarian and Biological Sciences

License

Copyright (c) 2025 Roberto Carlos Negreiros de Arruda; Karlos Yuri Fernandes Pedrosa ; Marco Antônio Gomes de Freitas Santos; Marianna Rodrigues Negreiros; Mylena Andréa Oliveira Torres; Isabella Rodrigues Negreiros ; Francisco Carneiro Lima; José Wendel Araujo Soares

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors who publish with this journal agree to the following terms:

1) Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.

2) Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.

3) Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.