Desenvolvimentos em inteligência artificial na avicultura de frangos de corte
Resumo
A indústria avícola brasileira vem ganhando destaque como grande produtor e ocupa o topo de colocações no ranking mundial. Na presente revisão, a partir de um levantamento exploratório, são apresentados resultados de pesquisas que utilizem Inteligência Artificial (IA) ao longo da cadeia de produção de frangos de corte, desde a análise de comportamentos para identificação de doenças e predição de peso de abate, ao controle de qualidade. Fazendo o período de estudo coincidir com o início internacional da Indústria 4.0, são relatados 26 trabalhos que apontam para a adoção crescente da IA e suas divisões.
Palavras-chave:
Zootecnia de precisão, inteligência artificial, aprendizagem de máquina, zootecnia 4.0, bem-estar animal
Referências
AMRAEI, S.; MEHDIZADEH, S. A.; NAAS, I. A. Development of a transfer function for weight prediction of live broiler chicken using machine vision. Engenharia Agrícola, v.38, p. 776-782, 2018. https://doi.org/10.1590/1809-4430-eng.agric.v38n5p776-782/2018
ASSOCIAÇÃO BRASILEIRA DE PROTEÍNA ANIMAL. Relatório anual 2021. 2021.Disponível em: <https://abpa-br.org/relatorios/>. Acesso em: 18/ago. 2021.
ASTILL, J. et al.. Smart poultry management: smart sensors, big data, and the internet of things. Computers and Electronics in Agriculture, v. 170, 105291, 2020. https://doi.org/10.1016/j.compag.2020.105291
ASTILL, J. et al. Detecting and predicting emerging disease in poultry with the implementation of new technologies and big data. Frontiers in Veterinary Science, v.5, 263, 2018.
BANAKAR, A.; SADEGHI, M.; SHUSHTARI, A. An intelligent device for diagnosing avian diseases: Newcastle, infectious bronchitis, avian influenza. Computers and Electronics in Agriculture, v. 127, p. 744-753, 2016. https://doi.org/10.1016/j.compag.2016.08.006
BARBON, S. et al. Machine Learning Applied to Near-Infrared Spectra for Chicken Meat Classification. Journal of Spectroscopy, v. 2018, 2018. https://doi.org/10.1155/2018/8949741
BASSI, N. S. S.; SILVA, C. L. Oportunidades e desafios para a cadeia produtiva de frangos de corte brasileira. Avicultura Industrial, Itu, ed. 1271, ano 109, n. 10, p.16-21, 2017.
BASSI, N. S. S.; SILVA, C. L. Pesquisa e Desenvolvimento na Cadeia Produtiva de Frangos de Corte no Brasil. Revista de Economia e Sociologia Rural [online], v. 56, n. 3, p.467-482, 2018. https://doi.org/10.1590/1234-56781806-94790560307
BRASIL. Agência Brasileira de Desenvolvimento Industrial. ABDI divulga selecionados do Edital Agro 4.0. 2020a. Disponível em: [link]. Acesso em: 18 ago. 2021.
BRASIL. Financiadora de Estudos e Projetos. Seleção pública MCTI/FINEP/FNDCT. Subvenção econômica à inovação - tecnologias 4.0, 2020b. Disponível em: [link]. Acesso em: 22 ago. 2021.
CUAN, K. et al. Detection of avian influenza-infected chickens based on a chicken sound convolutional neural network. Computers and Electronics in Agriculture, v. 178, id.105688, 2020. https://doi.org/10.1016/j.compag.2020.105688
DOUROU, D. et al. Rapid microbial quality assessment of chicken liver inoculated or not with salmonella using FTIR spectroscopy and machine learning. Frontiers in Microbiology, v. 11, 2021. https://doi.org/10.3389/fmicb.2020.623788
FAJAR, Y. Artificial neural network for healthy chicken meat identification. Int. Journal of Artificial Intelligence, v. 7, n. 1, p. 63-70, 2018. https://doi.org/10.11591/ijai.v7.i1.pp63-70
FANG, C. et al.. Pose estimation and behavior classification of broiler chickens based on deep neural networks. Computers and Electronics in Agriculture, v. 180, id. 105863,2021. https://doi.org/10.1016/j.compag.2020.105863
KAMRUZZAMAN, M., MAKINO, Y., OSHITA, S. Rapid and non-destructive detection of chicken adulteration in minced beef using visible near-infrared hyperspectral imaging and machine learning. Journal of food engineering, v. 170, p. 8-15, 2016. https://doi.org/10.1016/j.jfoodeng.2015.08.023
KOLLENBURG, G. et al. Dataset of the application of handheld NIR and machine learning for chicken fillet authenticity study. Data in Brief, v. 29, 2020. https://doi.org/10.1016/j.dib.2020.105357
MILOSEVIC, B. et al. Machine learning application in growth and health prediction of broiler chickens. World's Poultry Science Journal, v. 75, p. 401-410, 2019. https://doi.org/10.1017/S0043933919000254
MORTENSEN, A. K.; LISOUSKI, P.; AHRENDT, P. Weight prediction of broiler chickens using 3D computer vision. Computers and Electronics in Agriculture, v. 123, p. 319-326, 2016. https://doi.org/10.1016/j.compag.2016.03.011
MOTHER, D. et al. Preferred reporting items for systematic reviews and meta-analysis: the PRISMA statement. PLoS Med, 6, e1000097, 2009. https://doi.org/10.1371/journal.pmed.1000097
NYALALA, I. et al. Weight and volume estimation of poultry and products based on camera vision systems: a review. Poultry science, v. 100, n. 5, id. 101072, 2021. https://doi.org/10.1016/j.psj.2021.101072
OKINDA, C. et al. A review on computer vision systems in monitoring of poultry: a welfare perspective. Artificial Intelligence in Agriculture, v. 4, p. 184-208, 2020. https://doi.org/10.1016/j.aiia.2020.09.002
OLIVEIRA, F. D.; SANTANA, A.; PEREIRA, L. F. A. Impactos da IoT na Avicultura:um Mapeamento Sistemático. In: XII Congresso Brasileiro de Agroinformática,Indaiatuba, p. 418-427, 2019.
PASSAFARO, T. L. et al. Would large dataset sample size unveil the potential of deep neural networks for improved genome-enabled prediction of complex traits? the case of broiler weight prediction. BMC Genomics, v. 21, 771, 2020. https://doi.org/10.1186/s12864-020-07181-x
PEREZ, N. et al. Classification of Chicken Parts Using a Portable Near-Infrared (NIR) Spectrophotometer and Machine Learning. Applied spectroscopy, v. 72, n. 12, p.1774-1780, 2018. https://doi.org/10.1177/0003702818788878
POLEWKO-KLIM, A. et al. Sensitivity analysis based on random forest machine learning algorithm identifies candidate genes for regulation of innane and adaptive immune response of chicken. Poultry Science, v. 99, n. 12, p. 6341-6354, 2020. https://doi.org/10.1016/j.psj.2020.08.059
RIBEIRO, R. et al. Generating action plans for poultry management using artificial neural networks. Computers and Electronics in Agriculture, v. 161, p. 131-140, 2019. https://doi.org/doi.org/10.1016/j.compag.2018.02.017
ROCHA, G. B.; SILVA, I. J. O. A dinâmica da Zootecnia de Precisão e tecnologias de suporte atuais. 2020. Disponível em: [link]. Acesso em: 18 ago.2021.
ROWE, E.; DAWKINS, M. S.; GEBHARDT-HENRICH, S. G. A systematic review of precision livestock farming in the poultry sector: is technology focussed on improving bird welfare? Animals, v. 9, 614. doi:10.3390/ani9090614, 2019. https://doi.org/10.3390/ani9090614
SANTANA, L.; OLIVEIRA, J. Agricultura 4.0 e o desenvolvimento de pesquisas de computação aplicada às ciências agrárias. SBC Horizontes, marco. 2021. ISSN 2175-9235. Disponível em: [link]. Acesso em: 18 ago. 2021.
SCHWAB, K. The fourth industrial revolution. World Economic Forum, 2016.
SEO, D. et al. Identification of target chicken populations by machine learning models using minimum number of SNPs. Animals, v. 11, n. 241, 2021. https://doi.org/10.3390/ani11010241
WANG, C.; CHEN, Y.; CHIEN, C. Industry 3.5 to empower smart production for poultry farming and an empirical study for broiler live weight prediction. Computers &Industrial Engineering, v. 151, id. 106931, 2021. https://doi.org/10.1016/j.cie.2020.106931
YOUSEFINAGHANI, S. et al. A framework for the risk prediction of avian influenza occurrence: An Indonesian case study. PLoS ONE, doi: https://doi.org/10.1371/journal.pone.0245116, 2021. https://doi.org/10.1371/journal.pone.0245116
YUSOF, R. et al. Image segmentation and verification based on machine learning for vision inspection of chicken slaughtering. Journal of Physics, v. 1147, n. 1, 2020. https://doi.org/10.1088/1742-6596/1447/1/012024
YOU, J. et al. A supervised machine learning method to detect anomalous real-time broiler breeder body weight data recorded by a precision feeding system. Computers and Electronics in Agriculture, v. 185, 106171, 2021." https://doi.org/10.1016/j.compag.2021.106171
ZHUANG, X. et al. Development of an early warning algorithm to detect sick broilers. Computers and Electronics in Agriculture, v. 144, p. 102-113, 2018. https://doi.org/10.1016/j.compag.2017.11.032
ASSOCIAÇÃO BRASILEIRA DE PROTEÍNA ANIMAL. Relatório anual 2021. 2021.Disponível em: <https://abpa-br.org/relatorios/>. Acesso em: 18/ago. 2021.
ASTILL, J. et al.. Smart poultry management: smart sensors, big data, and the internet of things. Computers and Electronics in Agriculture, v. 170, 105291, 2020. https://doi.org/10.1016/j.compag.2020.105291
ASTILL, J. et al. Detecting and predicting emerging disease in poultry with the implementation of new technologies and big data. Frontiers in Veterinary Science, v.5, 263, 2018.
BANAKAR, A.; SADEGHI, M.; SHUSHTARI, A. An intelligent device for diagnosing avian diseases: Newcastle, infectious bronchitis, avian influenza. Computers and Electronics in Agriculture, v. 127, p. 744-753, 2016. https://doi.org/10.1016/j.compag.2016.08.006
BARBON, S. et al. Machine Learning Applied to Near-Infrared Spectra for Chicken Meat Classification. Journal of Spectroscopy, v. 2018, 2018. https://doi.org/10.1155/2018/8949741
BASSI, N. S. S.; SILVA, C. L. Oportunidades e desafios para a cadeia produtiva de frangos de corte brasileira. Avicultura Industrial, Itu, ed. 1271, ano 109, n. 10, p.16-21, 2017.
BASSI, N. S. S.; SILVA, C. L. Pesquisa e Desenvolvimento na Cadeia Produtiva de Frangos de Corte no Brasil. Revista de Economia e Sociologia Rural [online], v. 56, n. 3, p.467-482, 2018. https://doi.org/10.1590/1234-56781806-94790560307
BRASIL. Agência Brasileira de Desenvolvimento Industrial. ABDI divulga selecionados do Edital Agro 4.0. 2020a. Disponível em: [link]. Acesso em: 18 ago. 2021.
BRASIL. Financiadora de Estudos e Projetos. Seleção pública MCTI/FINEP/FNDCT. Subvenção econômica à inovação - tecnologias 4.0, 2020b. Disponível em: [link]. Acesso em: 22 ago. 2021.
CUAN, K. et al. Detection of avian influenza-infected chickens based on a chicken sound convolutional neural network. Computers and Electronics in Agriculture, v. 178, id.105688, 2020. https://doi.org/10.1016/j.compag.2020.105688
DOUROU, D. et al. Rapid microbial quality assessment of chicken liver inoculated or not with salmonella using FTIR spectroscopy and machine learning. Frontiers in Microbiology, v. 11, 2021. https://doi.org/10.3389/fmicb.2020.623788
FAJAR, Y. Artificial neural network for healthy chicken meat identification. Int. Journal of Artificial Intelligence, v. 7, n. 1, p. 63-70, 2018. https://doi.org/10.11591/ijai.v7.i1.pp63-70
FANG, C. et al.. Pose estimation and behavior classification of broiler chickens based on deep neural networks. Computers and Electronics in Agriculture, v. 180, id. 105863,2021. https://doi.org/10.1016/j.compag.2020.105863
KAMRUZZAMAN, M., MAKINO, Y., OSHITA, S. Rapid and non-destructive detection of chicken adulteration in minced beef using visible near-infrared hyperspectral imaging and machine learning. Journal of food engineering, v. 170, p. 8-15, 2016. https://doi.org/10.1016/j.jfoodeng.2015.08.023
KOLLENBURG, G. et al. Dataset of the application of handheld NIR and machine learning for chicken fillet authenticity study. Data in Brief, v. 29, 2020. https://doi.org/10.1016/j.dib.2020.105357
MILOSEVIC, B. et al. Machine learning application in growth and health prediction of broiler chickens. World's Poultry Science Journal, v. 75, p. 401-410, 2019. https://doi.org/10.1017/S0043933919000254
MORTENSEN, A. K.; LISOUSKI, P.; AHRENDT, P. Weight prediction of broiler chickens using 3D computer vision. Computers and Electronics in Agriculture, v. 123, p. 319-326, 2016. https://doi.org/10.1016/j.compag.2016.03.011
MOTHER, D. et al. Preferred reporting items for systematic reviews and meta-analysis: the PRISMA statement. PLoS Med, 6, e1000097, 2009. https://doi.org/10.1371/journal.pmed.1000097
NYALALA, I. et al. Weight and volume estimation of poultry and products based on camera vision systems: a review. Poultry science, v. 100, n. 5, id. 101072, 2021. https://doi.org/10.1016/j.psj.2021.101072
OKINDA, C. et al. A review on computer vision systems in monitoring of poultry: a welfare perspective. Artificial Intelligence in Agriculture, v. 4, p. 184-208, 2020. https://doi.org/10.1016/j.aiia.2020.09.002
OLIVEIRA, F. D.; SANTANA, A.; PEREIRA, L. F. A. Impactos da IoT na Avicultura:um Mapeamento Sistemático. In: XII Congresso Brasileiro de Agroinformática,Indaiatuba, p. 418-427, 2019.
PASSAFARO, T. L. et al. Would large dataset sample size unveil the potential of deep neural networks for improved genome-enabled prediction of complex traits? the case of broiler weight prediction. BMC Genomics, v. 21, 771, 2020. https://doi.org/10.1186/s12864-020-07181-x
PEREZ, N. et al. Classification of Chicken Parts Using a Portable Near-Infrared (NIR) Spectrophotometer and Machine Learning. Applied spectroscopy, v. 72, n. 12, p.1774-1780, 2018. https://doi.org/10.1177/0003702818788878
POLEWKO-KLIM, A. et al. Sensitivity analysis based on random forest machine learning algorithm identifies candidate genes for regulation of innane and adaptive immune response of chicken. Poultry Science, v. 99, n. 12, p. 6341-6354, 2020. https://doi.org/10.1016/j.psj.2020.08.059
RIBEIRO, R. et al. Generating action plans for poultry management using artificial neural networks. Computers and Electronics in Agriculture, v. 161, p. 131-140, 2019. https://doi.org/doi.org/10.1016/j.compag.2018.02.017
ROCHA, G. B.; SILVA, I. J. O. A dinâmica da Zootecnia de Precisão e tecnologias de suporte atuais. 2020. Disponível em: [link]. Acesso em: 18 ago.2021.
ROWE, E.; DAWKINS, M. S.; GEBHARDT-HENRICH, S. G. A systematic review of precision livestock farming in the poultry sector: is technology focussed on improving bird welfare? Animals, v. 9, 614. doi:10.3390/ani9090614, 2019. https://doi.org/10.3390/ani9090614
SANTANA, L.; OLIVEIRA, J. Agricultura 4.0 e o desenvolvimento de pesquisas de computação aplicada às ciências agrárias. SBC Horizontes, marco. 2021. ISSN 2175-9235. Disponível em: [link]. Acesso em: 18 ago. 2021.
SCHWAB, K. The fourth industrial revolution. World Economic Forum, 2016.
SEO, D. et al. Identification of target chicken populations by machine learning models using minimum number of SNPs. Animals, v. 11, n. 241, 2021. https://doi.org/10.3390/ani11010241
WANG, C.; CHEN, Y.; CHIEN, C. Industry 3.5 to empower smart production for poultry farming and an empirical study for broiler live weight prediction. Computers &Industrial Engineering, v. 151, id. 106931, 2021. https://doi.org/10.1016/j.cie.2020.106931
YOUSEFINAGHANI, S. et al. A framework for the risk prediction of avian influenza occurrence: An Indonesian case study. PLoS ONE, doi: https://doi.org/10.1371/journal.pone.0245116, 2021. https://doi.org/10.1371/journal.pone.0245116
YUSOF, R. et al. Image segmentation and verification based on machine learning for vision inspection of chicken slaughtering. Journal of Physics, v. 1147, n. 1, 2020. https://doi.org/10.1088/1742-6596/1447/1/012024
YOU, J. et al. A supervised machine learning method to detect anomalous real-time broiler breeder body weight data recorded by a precision feeding system. Computers and Electronics in Agriculture, v. 185, 106171, 2021." https://doi.org/10.1016/j.compag.2021.106171
ZHUANG, X. et al. Development of an early warning algorithm to detect sick broilers. Computers and Electronics in Agriculture, v. 144, p. 102-113, 2018. https://doi.org/10.1016/j.compag.2017.11.032
Publicado
10/11/2021
Como Citar
SILVA, Lucas Gabriel Galdino da; CORDEIRO NETO, Francisco Gomes; OLIVEIRA, Josenalde; SANTANA, Laura.
Desenvolvimentos em inteligência artificial na avicultura de frangos de corte. In: CONGRESSO BRASILEIRO DE AGROINFORMÁTICA (SBIAGRO), 13. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 71-79.
ISSN 2177-9724.
DOI: https://doi.org/10.5753/sbiagro.2021.18377.
