VISÃO COMPUTACIONAL E MONITORAMENTO DO COMPORTAMENTO DE SUÍNOS
DOI:
https://doi.org/10.52138/citec.v17i01.409Keywords:
animal production, pig fram, animal welfare, i ImagesAbstract
Brazil is the fourth largest producer and exporter of pork in the world. The advancement of technologies, especially with the use of computer vision to identify the animal, marks a significant achievement in the analysis of animal behavior. Some behaviors may indicate animal health and welfare problems. Computer vision aims to reproduce the human ability to see, analyze, and understand images. The images can be captured by cameras and analyzed and processed by computer programs. The objective of this literature review was to contextualize computer vision and present its use in pig farming.
Downloads
References
ASSOCIAÇÃO BRASILEIRA DE PROTEÍNA ANIMAL (ABPA) Relatório anual 2023. 2023. Disponível em: https://abpa-br.org/wp-content/uploads/2023/04/Relatorio-Anual-2023.pdf. Acesso em: 12 out. 2024.
ALAMEER, A. et al. Automatic recognition of feeding and foraging behaviour in pigs using deep learning, Biosystems Engineering, 197, p. 91-104, 2020. ISSN 1537-5110, Disponível em: https://doi.org/10.1016/j.biosystemseng.2020.06.013. Acesso em: 12 out. 2024.
BACKES André Ricardo; JUNIOR SÁ, Jarbas Joaci de Mesquita. Introdução à visão computacional usando MATLAB. Rio de Janeiro: Alta books editora, 2016. ISBN 8550800236
BALLARD, Dana Harr; BROWN, Christopher. Computer vision, 1982. Prenice-Hall, Englewood Cliffs, NJ, 1982. Disponível em: https://archive.org/details/computervision0000ball. Acesso em: 12 out. 2024.
BARACHO, M.; TOLON, Y. Análise de imagens para avaliação do bem-estar animal. Agrarian Academy, 9, 17. 2022. Disponível em: https://conhecer.org.br/ojs/index.php/agrarian/article/view/5492. Acesso em: 12 out. 2024.
BHOJ, S. et al. Image processing strategies for pig liveweight measurement: Updates and challenges. Computers and Electronics in Agriculture, 193, 2022. 106693, ISSN 0168-1699, https://doi.org/10.1016/j.compag. 2022.106693. Acesso em: 12 out. 2024.
COELHO, Felipe de Moura. Inteligência Artificial aplicada ao agronegócio: uma revisão acerca dos resultados obtidos após sua implementação. Sorocaba, 2024. 37 p. Trabalho de conclusão de curso (Bacharelado - Engenharia Ambiental) - Universidade Estadual Paulista (UNESP), Instituto de Ciência e Tecnologia, Sorocaba. Orientador: Antonio Cesar Germano Martins
CHEN, C. et al. Recognition of aggressive episodes of pigs based on convolutional neural network and long short-term memory, Computers and Electronics in Agriculture, 169, p. 105-166, 2020. ISSN 0168-1699 Disponível em: https://doi.org/10.1016/j.compag.2019.105166. Acesso em: 12 out. 2024.
CHUANG M.A, C., DENG, M., YIN, Y. Pig face recognition based on improved YOLO light weight neural network, Information Processing in Agriculture. 11, Issue 3, p. 356-371, 2024. ISSN 2214-3173, Disponível em: https://doi.org/10.1016/j.inpa.2023.03.004. Acesso em: 12 out. 2024.
DOMINIAK, K.D. et al. Spatial modeling of pigs’ drinking patterns as an alarm reducing method I. Developing a multivariate dynamic linear model, Computers and Electronics in Agriculture, 161, p. 79-91, 2019. ISSN 0168-1699, Disponível em: https://doi.org/10.1016/j.compag.2018.06.032. Acesso em: 12 out. 2024.
DONG, Y. et al. Robust Piglet Nursing Behavior Monitoring through Multi-Modal Fusion of Computer Vision and Ambient Floor Vibration. Available at SSRN 4987438. 2024. Disponível em: https://ssrn.com/abstract=4987438. Acesso em: 12 out. 2024.
EMPRESA BRASILEIRA DE PESQUISA AGROPECUÁRIA(EMBRAPA). 2024. Carne suína - Portal Embrapa. Disponível em: https://www.embrapa.br/qualidade-da-carne/carne-suina. Acesso em: 12 out. 2024.
FERNANDES, A.F.A.; DÓREA, J. R. R.; ROSA, G. J. D. M. Image analysis and computer vision applications in animal sciences: an overview. Frontiers in Veterinary Science, 7, e551269, out. 2020. doi: 10.3389/fvets.2020.551269. Disponível em: https://www.frontiersin.org/articles/10.3389/fvets.2020.551269/full. Acesso em: 12 out. 2024.
FRAGOSO, Katherine Sharlene Barbosa; BUSS, Lizie Pereira. Bem-estar animal e sistemas de produção de suínos Tradução livre da Sessão 7, Capítulo 7.13 do Código Sanitário para Animais Terrestres – OIE 2018. Disponível em: https://www.gov.br/agricultura/pt-br/assuntos/producao-animal/arquivos/Captulo7.13emPortugus2020.pdf. Acesso em: 12 out. 2024.
FÖRSTNER, W.; WROBEL, B. P. Photogrammetric computer vision. Springer, 2016.
HANSNEN, F. M. et al. Towards on-farm pig face recognition using convolutional neural networks, Computers in Industry. 98, p. 145-152, ISSN 0166-3615, Disponível em: https://doi.org/10.1016/j.compind.2018.02.016. Acesso em: 12 out. 2024.
HOLM, E. A. et al. Overview: Computer Vision and Machine Learning for Microstructural Characterization and Analysis. Metallurgical and Materials Transactions A, 2020. Disponível em: doi: 10.1007/ s11661-020-06008-4. Acesso em: 12 out. 2024.
HOSSAIN, M.E. et al. A systematic review of machine learning techniques for cattle identification: datasets, methods, and future directions. Artificial Intelligence in Agriculture, Beijin, 6, p. 138–155, 2022. Disponível em: https://doi.org/10.1016/j.aiia.2022.09.002. Acesso em: 12 out. 2024.
JORGE, L. A. C.; INAMASU R. Y. Uso de veículos não tripulados (VANT) em agricultura de precisão, Agricultura de precisão: resultados de um novo olhar, pp 109-134. 2014. Disponível em: https://www.alice.cnptia.embrapa.br/alice/bitstream/doc/1003485/1/CAP8.pdf. Acesso em: 12 out. 2024.
KAUR, A. et al. Cattle identification with muzzle pattern using computer vision technology: a critical review and prospective. Soft Computing, New York, 26, n.10, p. 4771–4795, 2022. Disponível em: https://link.springer.com/article/10.1007/s00500-022-06935-x. Acesso em: 12 out. 2024.
KNAUER, M.T.; BAITINGER, D.J. The sow body condition caliper. Applied Engineering in Agriculture. 31: 175–178. 2015. Disponível em: doi: 10.13031/aea.31.10632. Acesso em: 12 out. 2024.
KHAN, S. et al. A guide to convolutional neural networks for computer vision. Synthesis Lectures on Computer Vision, Morgan & Claypool Publishers, 8, n. 1, p. 1–207, 2018. Disponível em: https://link.springer.com/book/10.1007/978-3-031-01821-3. Acesso em: 12 out. 2024.
KIM, S.H. et al. Animal situation tracking service using RFID, GPS, and sensors. In Proceedings of the 2010 IEEE Second International Conference on Computer and Network Technology (ICCNT), Bangkok, Thailand, 23–25 April 2010; pp. 153–156. Disponível em: https://doi.org/10.3390/s17122757. Acesso em: 12 out. 2024.
KIM, J. et al. Depth-Based Detection of Standing-Pigs in Moving Noise Environments. Sensors. 17, 2757. 2017. Disponível em: https://doi.org/10.3390/s17122757
LI, J. et al. Promote computer vision applications in pig farming scenarios: high-quality dataset, fundamental models, and comparable performance1, Journal of Integrative Agriculture, 2024, ISSN 2095-3119, Disponível em: https://doi.org/10.1016/j.jia.2024.08.014. Acesso em: 12 out. 2024.
MAHMUD, M.S. et al. A systematic literature review on deep learning applications for precision cattle farming. Computers and Electronics in Agriculture, Oxford, 187, 106313, Aug. 2021.Disponível em: https://doi.org/10.1016/j.compag.2021.106313. Acesso em: 12 out. 2024.
MARSOT., M. et al. An adaptive pig face recognition approach using Convolutional Neural Networks. 2020. Computers and Electronics in Agriculture,173, (2):105386 Disponível em: doi:10.1016/j.compag.2020.105386. Acesso em: 12 out. 2024.
MATTINA, M. et al. An efficient center-based method for real-time pig posture recognition and tracking. Applied Intelligence, 54, 5183–5196. 2024. Disponível em: https://doi.org/10.1007/s10489-024-05439-5. Acesso em: 12 out. 2024.
MILANO, D.; HONORATO, L. B. Visão Computacional. 2010. Artigo – Universidade Estadual de Campinas. Limeira, São Paulo, 2010. Disponível em: https://docplayer.com.br/3058305-Visao-computacional-danilo-de-milano-luciano-barrozohonorato-unicamp-universidade-estadual-de-campinas-ft-faculdade-de-tecnologia.html. Acesso em: 12 out. 2024.
NASIRAHMADI, A. et al. Automatic detection of mounting behaviours among pigs using image analysis. Computers and Electronics in Agriculture, 124, p.295–302, 2016. Disponível em: https://doi.org/10.1016/j.compag.2016.04.022. Acesso em: 12 out. 2024.
NASIRAHMADI, A. et al. A new approach for categorizing pig lying behaviour based on a Delaunay triangulation method. Animal, 11, p.131–139, 2017. Disponível em: https://doi.org/10.1017/S1751731116001208. Acesso em: 12 out. 2024.
NASIRAHMADI, A. et al. Using machine vision for investigation of changes in pig group lying patterns. Computers and Electronics in Agriculture, 119, p.184–190, 2015. Disponível em: https://doi.org/10.1016/j.compag.2015.10.023. Acesso em: 12 out. 2024.
NEVES, Hugo Vieira. Avanços em Visão Computacional / editores: Luiz Antônio Pereira Neves, Hugo Vieira Neto, Adilson Gonzaga. Curitiba, PR: Omnipax, 2012 406 p. ISBN: 978-85-64619-09-8. Acesso em: 12 out. 2024.
ORGANIZAÇÃO MUNDIAL DE SAÚDE ANIMAL (OIE) - World Organisation for Animal Health. Chapter 7.1: Introduction to the recommendations for animal welfare. In: Terrestrial Animal Health Code, volume 1, 2023. Disponível em: https://www.woah.org/en/what-we-do/standards/codes-andmanuals/terrestrial-code-onlineaccess/?id=169&L=1&htmfile=chapitre_aw_introduction.htm. Acesso em: 16 Ago. 2023
PANDORFI, H. et al. Suinocultura de precisão: Visão computacional e tecnologias digitais. Revista Científica de Produção Animal, 22, n.2, p.73-79, 2020. Disponível em: doi: http://dx.doi.org/10.5935/2176-4158/rcpa.v22n2p55-59. Acesso em: 12 out. 2024.
PEZZUOLO, A. et al. On-barn pig weight estimation based on body measurements by a Kinect v1 depth camera, Computers and Electronics in Agriculture. 2018. 148,
p. 29-36. 2018. ISSN 0168-1699, Disponível em: https://doi.org/10.1016/j.compag.2018.03.003. Acesso em: 12 out. 2024.
PITTMAN J.S. Sow Prolapse Syndrome; Proceedings of the ISU—James D. McKean Swine Disease Conference; Ames, IA, USA. 3–4 November 2016; pp. 45–58.
PSOTA, E.T. et al. Multi-Pig Part Detection and Association with a Fully-Convolutional Network Sensors, 19, 4, p.852, 2019.Disponível em: https://doi.org/10.3390/s19040852. Acesso em: 12 out. 2024.
REZA, M.N. et al. Instance Segmentation and Automated Pig Posture Recognition for Smart Health Management. Journal Animal Science Technology, 2024a.
Disponível em: https://doi.org/10.5187/jast.2024.e112. Acesso em: 12 out. 2024.
REZA, M.N. et al. Thermal imaging and computer vision technologies for the enhancement of pig husbandry: a review. Journal Animal Science Technology, 66, 1, 31-56. 2024b. Disponível em: doi: 10.5187/jast. 2024.e4. Epub 2024 Jan 31. PMID: 38618025; PMCID: PMC11007457. Acesso em: 12 out. 2024.
RIBEIRO, H. M. C. et al. Aplicação do OpenCV utilizando técnicas de visão computacional e segmentação de imagens para reconhecimento de colônias bacterianas em análises microbiológicas de qualidade de água. Caderno Pedagógico, 21,6, e4235. 2024. Disponível em: https://doi.org/10.54033/cadpedv21n6-040. Acesso em: 12 out. 2024.
RIEKERT, M. et al. Model selection for 24/7 pig position and posture detection by 2D camera imaging and deep learning 2021. Computers and Electronics in Agriculture Computers and Electronics in Agriculture, 187, p.106-213 2021. Disponível em: https://doi.org/10.1016/j.compag.2021.106213. Acesso em: 12 out. 2024.
SILVA, Fausto Oliveira. Classificador de imagens termográficas usando visão computacional / Fausto Oliveira e Silva. - 2023. 86 f.; il.; 30 cm Dissertação (mestrado) - Instituto Federal do Espírito Santo, Programa de Pós-graduação em Engenharia de Controle de Automação, 2023. Disponível em: https://repositorio.ifes.edu.br/bitstream/handle/123456789/3590/Disserta%c3%a7%c3%a3o_Final_Completa_Fausto.pdf?sequence=1&isAllowed=y. Acesso em: 12 out. 2024.
STAVRAKAKIS, S. et al. Validity of the Microsoft Kinect sensor for assessment of normal walking patterns in pigs. Computers Electronics in Agriculture, 117, p.1–7. 2015. Disponível em: doi: 10.1016/j.compag.2015.07.003. https://doi.org/10.1016/j.compag.2015.07.003Get rights and content. Acesso em: 12 out. 2024.
SUN, Y. et al. Deep Learning face representation from predicting 10000 classes[C]. Processings of IEEE Conference on Computer Vision and Pattern Recognition. 1891-1898. 2014. Disponível em: https://ieeexplore.ieee.org/document/6909640. Acesso em: 12 out. 2024.
TAIWO, G.et al. Vision Transformers for Automated Detection of Pig Interactions in Groups. Available at SSRN: 2024. Disponível em: https://ssrn.com/abstract=4994652 or http://dx.doi.org/10.2139/ssrn.4994652. Acesso em: 12 out. 2024.
TEFILI, D. et al. Supervisão de hábitos alimentares em grupos de animais com visão computacional em raspberry Pi. Brazilian Journal of Development, 10, 5, e69849. 2024. Disponível em: https://doi.org/10.34117/bjdv10n5-051. Acesso em: 12 out. 2024.
TIAN, M. et al. Chengjiang Long, Yuhao Ma, Automated pig counting using deep learning, Computers and Electronics in Agriculture, 2019. 163, 2019,104840, ISSN 0168-1699, Disponível em: https://doi.org/10.1016/j.compag.2019.05.049. Acesso em: 12 out. 2024.
WANG, K. et al. A portable and automatic Xtion-based measurement system for pig body size, Computers and Electronics in Agriculture, 2018. 148, p.291-298, 2018. ISSN 0168-1699, Disponível em: https://doi.org/10.1016/j.compag.2018.03.018. Acesso em: 12 out. 2024.
WANG, Y. et al. Walkthrough weighing of pigs using machine vision and an artificial neural network, Biosystems Engineering, 100, 01, p. 117-125, 2008. Disponível em: https://www.sciencedirect.com/science/article/abs/pii/S1537511008000500. Acesso em: 12 out. 2024.
WEBER, V. A. M. et al. Cattle weight estimation using active contour models andregression trees bagging. Computers and Electronics in Agriculture, Elsevier, 179, p.105804, 2020. Disponível em: https://doi.org/10.1016/j.compag.2020.105804. Acesso em: 12 out. 2024.
YANG, Q.; XIAO, D.; LIN, S. Feeding behavior recognition for group-housed pigs with the Faster R-CNN, Computers and Electronics in Agriculture, 155, p. 453-460, ISSN 0168-1699. 2018. Disponível em: https://doi.org/10.1016/j.compag.2018.11.002. Acesso em: 12 out. 2024.
YANG, Q. et al. Long-term video activity monitoring and anomaly alerting of group-housed pigs. Computers and Electronics in Agriculture, 224, p. 109-205. 2024. Disponível em: https://doi.org/10.1016/j.compag.2024.109205. Acesso em: 12 out. 2024.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Ciência & Tecnologia

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