Pecuária de Precisão com IoT e Machine Learning: Uma Revisão Sistemática
Resumo
O objetivo deste estudo é sintetizar uma taxonomia do estado da arte da Internet das Coisas e do Aprendizado de Máquina aplicados na Pecuária de Precisão. A revisão sistemática, conduzida segundo o protocolo PRISMA, explora trabalhos no intervalo 2015 e 2025 recuperados das bases IEEE Xplore e Scopus, que ofereceram em 2.282 artigos selecionados. Os resultados indicam predominância de pesquisas com bovinos e duas abordagens principais: visão computacional para análise comportamental e sensores vestíveis com algoritmos de Machine Learning para avaliação fisiológica. O estudo apresenta tendências emergentes e destaca desafios da Pecuária de Precisão, como a autonomia energética e as limitações de implantação em campo.
Palavras-chave:
Inteligência Artificial, Internet das Coisas, Aprendizado de Máquina
Referências
Abdullahi, M., Jimale, A., Ahmed, Y., and Nageye, A. (2024). Iot-based livestock tracking: Addressing challenges in somali livestock farming. International Journal of ADVANCED AND APPLIED SCIENCES, 11:84–91.
Amrani, S., Medani, K., Gherbi, C., and Mabed, H. (2025). Machine learning application in software-defined networking based agricultural internet of things: A systematic literature review. Cluster Computing.
Bauerdick, J., Spiekers, H., Bodensteiner, C., Theunissen, T., and Bernhardt, H. (2022). Concept of adding ground truth data for economic evaluation of silage quality in germany. 2022 ASABE Annual International Meeting.
Berckmans, D. (2017). General introduction to precision livestock farming. Animal Frontiers, 7(1):6–11.
Farhan, M., Wijaya Thaha, G. S., Kristiadi, E. A., and Mutijarsa, K. (2024). Cattle anomaly behavior detection system based on iot and computer vision in precision livestock farming. In 2024 International Conference on Information Technology Systems and Innovation (ICITSI). IEEE.
Faysal, M. A. H., Ahmed, M. R., Rahaman, M. M., and Ahmed, F. (2021). A review of groundbreaking changes in the poultry industry in bangladesh using the internet of things (iot) and computer vision technology. In 2021 International Conference on Automation, Control and Mechatronics for Industry 4.0 (ACMI). IEEE.
Higgins, J., Thomas, J., Chandler, J., Cumpston, M., Li, T., Page, M., and Welch, V. (2019). Cochrane Handbook for Systematic Reviews of Interventions. John Wiley & Sons, 2nd edition.
Kaur, D. and Kaur, A. (2022). IoT and Machine Learning-Based Systems for Predicting Cattle Health Status for Precision Livestock Farming. In 2022 International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON), pages 1–5.
Mahato, S. and Neethirajan, S. (2025). Dairy digid: An edge-cloud framework for real-time cattle biometrics and health classification. AI, 6(9).
Moher, D., Shamseer, L., Clarke, M., Ghersi, D., Liberati, A., Petticrew, M., Shekelle, P., and Stewart, L. A. (2015). Preferred reporting items for systematic review and meta-analysis protocols (prisma-p) 2015 statement. Systematic reviews, 4(1):1–9.
Parlin, A. F., Horning, N. A., Alstad, J. P., Cosentino, B. J., and Gibbs, J. P. (2025). Low-cost, lora gnss tracker for wildlife monitoring. HardwareX, 23:e00669.
Triantafyllou, A., Siniosoglou, I., Argyriou, V., Goudos, S. K., Papadopoulos, G. T., Panitsidis, K., and Sarigiannidis, P. (2025). Resource efficient federated lorawan architecture for far-edge iot applications. IEEE Access, 13:108766–108785.
Verma, S., Sharma, N., and Trivedi, G. (2025). Geotagging in livestock farming: Ai-based technologies, applications, and challenges. In Noor, A., Saroha, K., Pricop, E., Sen, A., and Trivedi, G., editors, Emerging Trends and Technologies on Intelligent Systems, pages 203–212, Singapore. Springer Nature Singapore.
Vuppalapati, C., Ilapakurti, A., Vissapragada, S., Vuppalapati, S., Kedari, S., Kedari, S., and Vuppalapati, J. (2025). Early detection of lameness in dairy cattle using activity data, image analysis, ai and ml - an approach for improved animal welfare and economic impact. Communications in Computer and Information Science.
Amrani, S., Medani, K., Gherbi, C., and Mabed, H. (2025). Machine learning application in software-defined networking based agricultural internet of things: A systematic literature review. Cluster Computing.
Bauerdick, J., Spiekers, H., Bodensteiner, C., Theunissen, T., and Bernhardt, H. (2022). Concept of adding ground truth data for economic evaluation of silage quality in germany. 2022 ASABE Annual International Meeting.
Berckmans, D. (2017). General introduction to precision livestock farming. Animal Frontiers, 7(1):6–11.
Farhan, M., Wijaya Thaha, G. S., Kristiadi, E. A., and Mutijarsa, K. (2024). Cattle anomaly behavior detection system based on iot and computer vision in precision livestock farming. In 2024 International Conference on Information Technology Systems and Innovation (ICITSI). IEEE.
Faysal, M. A. H., Ahmed, M. R., Rahaman, M. M., and Ahmed, F. (2021). A review of groundbreaking changes in the poultry industry in bangladesh using the internet of things (iot) and computer vision technology. In 2021 International Conference on Automation, Control and Mechatronics for Industry 4.0 (ACMI). IEEE.
Higgins, J., Thomas, J., Chandler, J., Cumpston, M., Li, T., Page, M., and Welch, V. (2019). Cochrane Handbook for Systematic Reviews of Interventions. John Wiley & Sons, 2nd edition.
Kaur, D. and Kaur, A. (2022). IoT and Machine Learning-Based Systems for Predicting Cattle Health Status for Precision Livestock Farming. In 2022 International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON), pages 1–5.
Mahato, S. and Neethirajan, S. (2025). Dairy digid: An edge-cloud framework for real-time cattle biometrics and health classification. AI, 6(9).
Moher, D., Shamseer, L., Clarke, M., Ghersi, D., Liberati, A., Petticrew, M., Shekelle, P., and Stewart, L. A. (2015). Preferred reporting items for systematic review and meta-analysis protocols (prisma-p) 2015 statement. Systematic reviews, 4(1):1–9.
Parlin, A. F., Horning, N. A., Alstad, J. P., Cosentino, B. J., and Gibbs, J. P. (2025). Low-cost, lora gnss tracker for wildlife monitoring. HardwareX, 23:e00669.
Triantafyllou, A., Siniosoglou, I., Argyriou, V., Goudos, S. K., Papadopoulos, G. T., Panitsidis, K., and Sarigiannidis, P. (2025). Resource efficient federated lorawan architecture for far-edge iot applications. IEEE Access, 13:108766–108785.
Verma, S., Sharma, N., and Trivedi, G. (2025). Geotagging in livestock farming: Ai-based technologies, applications, and challenges. In Noor, A., Saroha, K., Pricop, E., Sen, A., and Trivedi, G., editors, Emerging Trends and Technologies on Intelligent Systems, pages 203–212, Singapore. Springer Nature Singapore.
Vuppalapati, C., Ilapakurti, A., Vissapragada, S., Vuppalapati, S., Kedari, S., Kedari, S., and Vuppalapati, J. (2025). Early detection of lameness in dairy cattle using activity data, image analysis, ai and ml - an approach for improved animal welfare and economic impact. Communications in Computer and Information Science.
Publicado
04/12/2025
Como Citar
ALVES, Matusalen Costa; MOTA, Fabrício de Carvalho; PRADO, Cairon Ferreira; VALENTINO, Pedro Henrique; SANTOS, Iallen Gábio de Sousa; CRUZ, Jonathas Jivago de Almeida.
Pecuária de Precisão com IoT e Machine Learning: Uma Revisão Sistemática. In: ESCOLA REGIONAL DE COMPUTAÇÃO DO CEARÁ, MARANHÃO E PIAUÍ (ERCEMAPI), 13. , 2025, São Luís/MA.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 81-90.
DOI: https://doi.org/10.5753/ercemapi.2025.17411.
