Animal Monitoring Systems in Real-World Environments: A Literature Review of IoT and Computer Vision Technologies
Resumo
Esta Revisão Sistemática da Literatura (RSL) analisa tecnologias de Internet das Coisas (IoT) e Visão Computacional na Pecuária de Precisão, focando em Identificação por Radiofrequência (RFID), imagens e Edge Computing. Embora os sistemas apresentem alta acurácia em ambientes controlados, a aplicação em campo enfrenta severas limitações de energia, conectividade e oclusões visuais. Conclui-se que a fusão multimodal de dados é essencial para superar as falhas individuais dos sensores e viabilizar um manejo proativo.Referências
Aquilani, C., Confessore, A., Bozzi, R., Sirtori, F., and Pugliese, C. (2022). Precision livestock farming technologies in pasture-based livestock systems. Animal, 16(1):100429.
Araujo, M., Leitão, P., Castro, M., Castro, J., and Bernuy, M. (2024). Development of an iot-based device for data collection on sheep and goat herding in silvopastoral systems. Sensors, 24(17):5528.
Bergman, N., Yitzhaky, Y., and Halachmi, I. (2024). Biometric identification of dairy cows via real-time facial recognition. animal, 18(3):101079.
Bhujel, A., Wang, Y., Lu, Y., Morris, D., and Dangol, M. (2024). Public computer vision datasets for precision livestock farming: A systematic survey. arXiv preprint arXiv:2406.10628.
Chae, H., Lee, J., Kim, J., Lee, S., Lee, J., Chung, Y., and Park, D. (2024). Novel method for detecting coughing pigs with audio-visual multimodality for smart agriculture monitoring. Sensors, 24(22):7232.
Emsen, E. and Odevci, B. B. (2025). Smart ict tools for precision management in intensive sheep systems. In 2025 IEEE 19th International Conference on Application of Information and Communication Technologies (AICT), pages 1–5. IEEE.
Gabato, A. J., Vendivil, J., Sison, B. A., Botangen, K. A., Del Rosario, N., and Santiago, K. (2024). Development of an rfid-based record management web portal for goats towards precision livestock farming. In 2024 IEEE International Conference on Computing (ICOCO), pages 479–484. IEEE.
González-García, E., Alhamada, M., Nascimento, H., Portes, D., Bonnafe, G., Allain, C., Llach, I., Hassoun, P., Gautier, J.-M., and Parisot, S. (2021). Measuring liveweight changes in lactating dairy ewes with an automated walk-over-weighing system. Journal of dairy science, 104(5):5675–5688.
Lemmens, L., Schodl, K., Fuerst-Waltl, B., Schwarzenbacher, H., Egger-Danner, C., Linke, K., Suntinger, M., Phelan, M., Mayerhofer, M., Steininger, F., et al. (2023). The combined use of automated milking system and sensor data to improve detection of mild lameness in dairy cattle. Animals, 13(7):1180.
Muniz, G. M., Calheiros, S. G., Dias Filho, J. M., de Oliveira Feitoza, W., Postigo, M. A. O., and Torné, I. G. (2025). Bovtag 4.0: Solução iot para pecuária baseada em comunicação por rádio frequência e monitoramento via dashboard. In 2025 16th IEEE International Conference on Industry Applications (INDUSCON), pages 1775–1782. IEEE.
Mura, M. C., Othmane, T., Vincenzo, C., and Sebastiano, L. (2026). Precision livestock farming for dairy sheep: A literature review of iot and decision-support systems for enhanced management and welfare. AgriEngineering, 8(2):58.
Paolino, R., Di Trana, A., Coppola, A., Sabia, E., Riviezzi, A. M., Vignozzi, L., Claps, S., Caparra, P., Pacelli, C., and Braghieri, A. (2025). May the extensive farming system of small ruminants be smart? Agriculture, 15(9):929.
Shulkin, J. D., Vibhatasilpin, A., and Adhana, V. (2025). Pervasive sensing for livestock health and activity monitoring: Current methods and techniques. arXiv preprint arXiv:2503.15674.
Suhaimi, A. F., Mutiara, G. A., and Rizal, M. F. (2025). Leveraging embedded accelerometers and machine learning for real-time sheep behavior classification in precision farming. IEEE Access.
Sztandarski, P., Marchewka, J., Jaszczyk, A., Solka, M., Michnowska, H., Pogorzelski, G., Gotxi, J. R., Damaziak, K., Wójcik, W., Siwiec, D., et al. (2025). Scientific literature on ai technologies to enhance animal welfare, health and productivity. Animal Science Papers & Reports, 43(2).
Tzanidakis, C., Tzamaloukas, O., Simitzis, P., and Panagakis, P. (2023). Precision livestock farming applications (plf) for grazing animals. Agriculture, 13(2):288.
Vaintrub, M. O., Levit, H., Chincarini, M., Fusaro, I., Giammarco, M., and Vignola, G. (2021). Precision livestock farming, automats and new technologies: Possible applications in extensive dairy sheep farming. Animal, 15(3):100143.
Xue, J., Hou, Z., Xuan, C., Ma, Y., Sun, Q., Zhang, X., and Zhong, L. (2024). A sheep identification method based on three-dimensional sheep face reconstruction and feature point matching. Animals, 14(13):1923.
Yang, Q., Hui, X., Huang, Y., Chen, M., Huang, S., and Xiao, D. (2024). A long-term video tracking method for group-housed pigs. Animals, 14(10):1505.
Yaseer, A. and Chen, H. (2021). A review of sensors and machine learning in animal farming. In 2021 IEEE 11th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER), pages 747–752. IEEE.
Araujo, M., Leitão, P., Castro, M., Castro, J., and Bernuy, M. (2024). Development of an iot-based device for data collection on sheep and goat herding in silvopastoral systems. Sensors, 24(17):5528.
Bergman, N., Yitzhaky, Y., and Halachmi, I. (2024). Biometric identification of dairy cows via real-time facial recognition. animal, 18(3):101079.
Bhujel, A., Wang, Y., Lu, Y., Morris, D., and Dangol, M. (2024). Public computer vision datasets for precision livestock farming: A systematic survey. arXiv preprint arXiv:2406.10628.
Chae, H., Lee, J., Kim, J., Lee, S., Lee, J., Chung, Y., and Park, D. (2024). Novel method for detecting coughing pigs with audio-visual multimodality for smart agriculture monitoring. Sensors, 24(22):7232.
Emsen, E. and Odevci, B. B. (2025). Smart ict tools for precision management in intensive sheep systems. In 2025 IEEE 19th International Conference on Application of Information and Communication Technologies (AICT), pages 1–5. IEEE.
Gabato, A. J., Vendivil, J., Sison, B. A., Botangen, K. A., Del Rosario, N., and Santiago, K. (2024). Development of an rfid-based record management web portal for goats towards precision livestock farming. In 2024 IEEE International Conference on Computing (ICOCO), pages 479–484. IEEE.
González-García, E., Alhamada, M., Nascimento, H., Portes, D., Bonnafe, G., Allain, C., Llach, I., Hassoun, P., Gautier, J.-M., and Parisot, S. (2021). Measuring liveweight changes in lactating dairy ewes with an automated walk-over-weighing system. Journal of dairy science, 104(5):5675–5688.
Lemmens, L., Schodl, K., Fuerst-Waltl, B., Schwarzenbacher, H., Egger-Danner, C., Linke, K., Suntinger, M., Phelan, M., Mayerhofer, M., Steininger, F., et al. (2023). The combined use of automated milking system and sensor data to improve detection of mild lameness in dairy cattle. Animals, 13(7):1180.
Muniz, G. M., Calheiros, S. G., Dias Filho, J. M., de Oliveira Feitoza, W., Postigo, M. A. O., and Torné, I. G. (2025). Bovtag 4.0: Solução iot para pecuária baseada em comunicação por rádio frequência e monitoramento via dashboard. In 2025 16th IEEE International Conference on Industry Applications (INDUSCON), pages 1775–1782. IEEE.
Mura, M. C., Othmane, T., Vincenzo, C., and Sebastiano, L. (2026). Precision livestock farming for dairy sheep: A literature review of iot and decision-support systems for enhanced management and welfare. AgriEngineering, 8(2):58.
Paolino, R., Di Trana, A., Coppola, A., Sabia, E., Riviezzi, A. M., Vignozzi, L., Claps, S., Caparra, P., Pacelli, C., and Braghieri, A. (2025). May the extensive farming system of small ruminants be smart? Agriculture, 15(9):929.
Shulkin, J. D., Vibhatasilpin, A., and Adhana, V. (2025). Pervasive sensing for livestock health and activity monitoring: Current methods and techniques. arXiv preprint arXiv:2503.15674.
Suhaimi, A. F., Mutiara, G. A., and Rizal, M. F. (2025). Leveraging embedded accelerometers and machine learning for real-time sheep behavior classification in precision farming. IEEE Access.
Sztandarski, P., Marchewka, J., Jaszczyk, A., Solka, M., Michnowska, H., Pogorzelski, G., Gotxi, J. R., Damaziak, K., Wójcik, W., Siwiec, D., et al. (2025). Scientific literature on ai technologies to enhance animal welfare, health and productivity. Animal Science Papers & Reports, 43(2).
Tzanidakis, C., Tzamaloukas, O., Simitzis, P., and Panagakis, P. (2023). Precision livestock farming applications (plf) for grazing animals. Agriculture, 13(2):288.
Vaintrub, M. O., Levit, H., Chincarini, M., Fusaro, I., Giammarco, M., and Vignola, G. (2021). Precision livestock farming, automats and new technologies: Possible applications in extensive dairy sheep farming. Animal, 15(3):100143.
Xue, J., Hou, Z., Xuan, C., Ma, Y., Sun, Q., Zhang, X., and Zhong, L. (2024). A sheep identification method based on three-dimensional sheep face reconstruction and feature point matching. Animals, 14(13):1923.
Yang, Q., Hui, X., Huang, Y., Chen, M., Huang, S., and Xiao, D. (2024). A long-term video tracking method for group-housed pigs. Animals, 14(10):1505.
Yaseer, A. and Chen, H. (2021). A review of sensors and machine learning in animal farming. In 2021 IEEE 11th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER), pages 747–752. IEEE.
Publicado
19/10/2026
Como Citar
SILVA, Rita de Cássia Rodrigues da; ARAÚJO, Flávio Henrique Duarte de; MARQUES, Júlio Vítor Monteiro; VELOSO E SILVA, Romuere Rodrigues.
Animal Monitoring Systems in Real-World Environments: A Literature Review of IoT and Computer Vision Technologies. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 23. , 2026, Cuiabá/MT.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 1982-1993.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2026.31476.
