Inteligência artificial na avaliação de respostas produtivas e fisiológicas de frangos de corte submetidos a diferentes intensidades e durações de estresse térmico
Autor(a) principal: | |
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Data de Publicação: | 2015 |
Tipo de documento: | Tese |
Idioma: | por |
Título da fonte: | Repositório Institucional da UFLA |
Texto Completo: | http://repositorio.ufla.br/jspui/handle/1/10621 |
Resumo: | In aviculture, the thermal environment is responsible for homeothermic process of poultry, and, when subjected to some kind of heat stress their comfort is affected thus compromising the productive performance. Thus, this research aimed to analyze the productive responses, cloacal temperature (t clo, ° C) and surface temperature (t sup , ° C) of broilers submitted to different intensities and durations of air dry-bulb temperature (t db, ° C) throughout their second week of life. An experiment was conducted in the Animal Ambience Laboratory of the Federal University of Lavras, equipped with four air-conditioned wind tunnels that have recirculation and partial air renewal. It was used 240 broilers divided into four stages, where in the second week, for each step there was a different t bs (24, 27, 30 and 33 °C) with a different duration (1, 2, 3 and 4 days). The relative humidity and air velocity were fixed at 60% and 0.2 m s -1 , respectively. In the first and third experimental week, the birds were subjected to thermal comfort conditions, characterized in tdb values of 33 °C and 27 °C respectively. Variance analysis was used to analyze the effect of temperature fluctuations and its duration. Mathematical models have been developed using the fuzzy logic and artificial neural networks (ANN) in which it was possible to predict the t clo , feed conversion (FC, g) and water consumption (C water , ml) depending on the intensities and durations. The results obtained with productive responses showed that in a tdb of 24 ° C (low temperature stress) poultry had more feed intake but obtained a worst feed conversion. Best feed conversion was obtained in poultry submitted to a tdb of 30 °C. It was seen that with tdb of 24 and 27 °C there was a reduction in tclo and tsup , where poultry acclimatization to heat stress occurred from the second day of stress. The tclo values simulated by the fuzzy model had standard deviations and smaller percentage errors of 0.02 and 0.08%, respectively, than those obtained experimentally. For the ANN developed, the coefficients of determination (R 2 ) for tclo, FC and C water were 0.87; 0.79 and 0.97, respectively. These results demonstrated that the templates had high predictive power and could be used to support decision making in control of thermal environment systems. |
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Inteligência artificial na avaliação de respostas produtivas e fisiológicas de frangos de corte submetidos a diferentes intensidades e durações de estresse térmicoArtificial intelligence in the assessment of production and physiological responses in broiler submitted to different intensities and duration of heat stressDesempenhoTemperatura cloacalTemperatura superficialLógica fuzzyRedes neurais artificiaisAviculturaAmbiênciaPerformanceCloacal temperatureSurface temperatureFuzzy logicArtificial neural networksAvicultureAmbienceEngenharia AgrícolaIn aviculture, the thermal environment is responsible for homeothermic process of poultry, and, when subjected to some kind of heat stress their comfort is affected thus compromising the productive performance. Thus, this research aimed to analyze the productive responses, cloacal temperature (t clo, ° C) and surface temperature (t sup , ° C) of broilers submitted to different intensities and durations of air dry-bulb temperature (t db, ° C) throughout their second week of life. An experiment was conducted in the Animal Ambience Laboratory of the Federal University of Lavras, equipped with four air-conditioned wind tunnels that have recirculation and partial air renewal. It was used 240 broilers divided into four stages, where in the second week, for each step there was a different t bs (24, 27, 30 and 33 °C) with a different duration (1, 2, 3 and 4 days). The relative humidity and air velocity were fixed at 60% and 0.2 m s -1 , respectively. In the first and third experimental week, the birds were subjected to thermal comfort conditions, characterized in tdb values of 33 °C and 27 °C respectively. Variance analysis was used to analyze the effect of temperature fluctuations and its duration. Mathematical models have been developed using the fuzzy logic and artificial neural networks (ANN) in which it was possible to predict the t clo , feed conversion (FC, g) and water consumption (C water , ml) depending on the intensities and durations. The results obtained with productive responses showed that in a tdb of 24 ° C (low temperature stress) poultry had more feed intake but obtained a worst feed conversion. Best feed conversion was obtained in poultry submitted to a tdb of 30 °C. It was seen that with tdb of 24 and 27 °C there was a reduction in tclo and tsup , where poultry acclimatization to heat stress occurred from the second day of stress. The tclo values simulated by the fuzzy model had standard deviations and smaller percentage errors of 0.02 and 0.08%, respectively, than those obtained experimentally. For the ANN developed, the coefficients of determination (R 2 ) for tclo, FC and C water were 0.87; 0.79 and 0.97, respectively. These results demonstrated that the templates had high predictive power and could be used to support decision making in control of thermal environment systems.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Na avicultura, o ambiente térmico é responsável pelo processo homeotérmico das aves, sendo que, quando submetidas a algum tipo de estresse térmico seu conforto é afetado, comprometendo assim o desempenho produtivo. Desse modo, a presente pesquisa foi conduzida com o objetivo de analisar as respostas produtivas, a temperatura cloacal (tclo , °C) e temperatura superficial (tsup , °C) de frangos de corte, submetidos a diferentes intensidades e durações de temperatura de bulbo seco do ar (t bs, °C), na segunda semana de vida. Para isto, foi conduzido um experimento no Laboratório de Ambiência Animal da Universidade Federal de Lavras, equipado com quatro túneis de vento climatizados que possuem recirculação e renovação parcial do ar. Foram utilizados 240 frangos de corte, divididos em quatro etapas, sendo que, na segunda semana, para cada etapa foram utilizadas diferentes t bs (24, 27, 30 e 33 °C) e durações (1, 2, 3 e 4 dias). A umidade relativa do ar e a velocidade do ar foram fixadas em 60 % e 0,2 m s -1 , respectivamente. Na primeira e terceira semanas experimentais, as aves foram submetidas a condições de conforto térmico, caracterizados por valores de t bs de 33 °C e 27 °C, respectivamente. Para analisar o efeito das variações térmicas e suas durações, foi utilizada a análise de variância. Modelos matemáticos foram desenvolvidos utilizando lógica fuzzy e redes neurais artificiais (RNA), em que, foi possível predizer a tclo , conversão alimentar (CA, g) e o consumo de água (Cágua , ml) em função das intensidades e durações de t bs. Com os resultados obtidos por meio das respostas produtivas, verificou-se que, para a t bs de 24 °C (estresse por baixa temperatura) as aves consumiram mais ração, porém, obtiveram a pior CA. As aves que demonstraram a melhor CA foram submetidas a tbs de 30 °C. Verificou-se que para a t bs de 24 e 27 °C houve diminuição da t clo e t sup , sendo que a aclimatação das aves ao estresse térmico ocorreu a partir do segundo dia de estresse. Os valores de t clo simulados pelo modelo fuzzy apresentaram desvios padrão e erros percentuais menores que 0,02 e 0,08 %, respectivamente, quando comparados aos obtidos experimentalmente. Para a RNA desenvolvida, os coeficientes de determinação (R 2 ) para a tclo, CA e C água foram de 0,87; 0,79 e 0,97, respectivamente. Estes resultados indicam que os modelos possuem alta capacidade preditiva e podem ser utilizados como suporte à tomada de decisão em sistemas de controle do ambiente térmico.Universidade Federal de LavrasPrograma de Pós-Graduação em Engenharia AgrícolaUFLAbrasilDepartamento de EngenhariaYanagi Junior, TadayukiCampos, Alessandro TorresLima, Renato RibeiroCampos, Alessandro TorresFassani, Édison JoséDamasceno, Flávio AlvesTinôco, Ilda de Fátima FerreiraFassani, Édison JoséAbreu, Lucas Henrique Pedrozo2015-11-27T16:45:42Z2015-11-27T16:45:42Z2015-11-272015-11-06info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisapplication/pdfABREU, L. H. P. Inteligência artificial na avaliação de respostas produtivas e fisiológicas de frangos de corte submetidos a diferentes intensidades e durações de estresse térmico. 2015. 163 p. Tese (Doutorado em Engenharia Agrícola)-Universidade Federal de Lavras, Lavras, 2015.http://repositorio.ufla.br/jspui/handle/1/10621porinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFLAinstname:Universidade Federal de Lavras (UFLA)instacron:UFLA2023-05-02T12:55:24Zoai:localhost:1/10621Repositório InstitucionalPUBhttp://repositorio.ufla.br/oai/requestnivaldo@ufla.br || repositorio.biblioteca@ufla.bropendoar:2023-05-02T12:55:24Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)false |
dc.title.none.fl_str_mv |
Inteligência artificial na avaliação de respostas produtivas e fisiológicas de frangos de corte submetidos a diferentes intensidades e durações de estresse térmico Artificial intelligence in the assessment of production and physiological responses in broiler submitted to different intensities and duration of heat stress |
title |
Inteligência artificial na avaliação de respostas produtivas e fisiológicas de frangos de corte submetidos a diferentes intensidades e durações de estresse térmico |
spellingShingle |
Inteligência artificial na avaliação de respostas produtivas e fisiológicas de frangos de corte submetidos a diferentes intensidades e durações de estresse térmico Abreu, Lucas Henrique Pedrozo Desempenho Temperatura cloacal Temperatura superficial Lógica fuzzy Redes neurais artificiais Avicultura Ambiência Performance Cloacal temperature Surface temperature Fuzzy logic Artificial neural networks Aviculture Ambience Engenharia Agrícola |
title_short |
Inteligência artificial na avaliação de respostas produtivas e fisiológicas de frangos de corte submetidos a diferentes intensidades e durações de estresse térmico |
title_full |
Inteligência artificial na avaliação de respostas produtivas e fisiológicas de frangos de corte submetidos a diferentes intensidades e durações de estresse térmico |
title_fullStr |
Inteligência artificial na avaliação de respostas produtivas e fisiológicas de frangos de corte submetidos a diferentes intensidades e durações de estresse térmico |
title_full_unstemmed |
Inteligência artificial na avaliação de respostas produtivas e fisiológicas de frangos de corte submetidos a diferentes intensidades e durações de estresse térmico |
title_sort |
Inteligência artificial na avaliação de respostas produtivas e fisiológicas de frangos de corte submetidos a diferentes intensidades e durações de estresse térmico |
author |
Abreu, Lucas Henrique Pedrozo |
author_facet |
Abreu, Lucas Henrique Pedrozo |
author_role |
author |
dc.contributor.none.fl_str_mv |
Yanagi Junior, Tadayuki Campos, Alessandro Torres Lima, Renato Ribeiro Campos, Alessandro Torres Fassani, Édison José Damasceno, Flávio Alves Tinôco, Ilda de Fátima Ferreira Fassani, Édison José |
dc.contributor.author.fl_str_mv |
Abreu, Lucas Henrique Pedrozo |
dc.subject.por.fl_str_mv |
Desempenho Temperatura cloacal Temperatura superficial Lógica fuzzy Redes neurais artificiais Avicultura Ambiência Performance Cloacal temperature Surface temperature Fuzzy logic Artificial neural networks Aviculture Ambience Engenharia Agrícola |
topic |
Desempenho Temperatura cloacal Temperatura superficial Lógica fuzzy Redes neurais artificiais Avicultura Ambiência Performance Cloacal temperature Surface temperature Fuzzy logic Artificial neural networks Aviculture Ambience Engenharia Agrícola |
description |
In aviculture, the thermal environment is responsible for homeothermic process of poultry, and, when subjected to some kind of heat stress their comfort is affected thus compromising the productive performance. Thus, this research aimed to analyze the productive responses, cloacal temperature (t clo, ° C) and surface temperature (t sup , ° C) of broilers submitted to different intensities and durations of air dry-bulb temperature (t db, ° C) throughout their second week of life. An experiment was conducted in the Animal Ambience Laboratory of the Federal University of Lavras, equipped with four air-conditioned wind tunnels that have recirculation and partial air renewal. It was used 240 broilers divided into four stages, where in the second week, for each step there was a different t bs (24, 27, 30 and 33 °C) with a different duration (1, 2, 3 and 4 days). The relative humidity and air velocity were fixed at 60% and 0.2 m s -1 , respectively. In the first and third experimental week, the birds were subjected to thermal comfort conditions, characterized in tdb values of 33 °C and 27 °C respectively. Variance analysis was used to analyze the effect of temperature fluctuations and its duration. Mathematical models have been developed using the fuzzy logic and artificial neural networks (ANN) in which it was possible to predict the t clo , feed conversion (FC, g) and water consumption (C water , ml) depending on the intensities and durations. The results obtained with productive responses showed that in a tdb of 24 ° C (low temperature stress) poultry had more feed intake but obtained a worst feed conversion. Best feed conversion was obtained in poultry submitted to a tdb of 30 °C. It was seen that with tdb of 24 and 27 °C there was a reduction in tclo and tsup , where poultry acclimatization to heat stress occurred from the second day of stress. The tclo values simulated by the fuzzy model had standard deviations and smaller percentage errors of 0.02 and 0.08%, respectively, than those obtained experimentally. For the ANN developed, the coefficients of determination (R 2 ) for tclo, FC and C water were 0.87; 0.79 and 0.97, respectively. These results demonstrated that the templates had high predictive power and could be used to support decision making in control of thermal environment systems. |
publishDate |
2015 |
dc.date.none.fl_str_mv |
2015-11-27T16:45:42Z 2015-11-27T16:45:42Z 2015-11-27 2015-11-06 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
format |
doctoralThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
ABREU, L. H. P. Inteligência artificial na avaliação de respostas produtivas e fisiológicas de frangos de corte submetidos a diferentes intensidades e durações de estresse térmico. 2015. 163 p. Tese (Doutorado em Engenharia Agrícola)-Universidade Federal de Lavras, Lavras, 2015. http://repositorio.ufla.br/jspui/handle/1/10621 |
identifier_str_mv |
ABREU, L. H. P. Inteligência artificial na avaliação de respostas produtivas e fisiológicas de frangos de corte submetidos a diferentes intensidades e durações de estresse térmico. 2015. 163 p. Tese (Doutorado em Engenharia Agrícola)-Universidade Federal de Lavras, Lavras, 2015. |
url |
http://repositorio.ufla.br/jspui/handle/1/10621 |
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por |
language |
por |
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info:eu-repo/semantics/openAccess |
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openAccess |
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application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Federal de Lavras Programa de Pós-Graduação em Engenharia Agrícola UFLA brasil Departamento de Engenharia |
publisher.none.fl_str_mv |
Universidade Federal de Lavras Programa de Pós-Graduação em Engenharia Agrícola UFLA brasil Departamento de Engenharia |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da UFLA instname:Universidade Federal de Lavras (UFLA) instacron:UFLA |
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Universidade Federal de Lavras (UFLA) |
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UFLA |
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UFLA |
reponame_str |
Repositório Institucional da UFLA |
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Repositório Institucional da UFLA |
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Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA) |
repository.mail.fl_str_mv |
nivaldo@ufla.br || repositorio.biblioteca@ufla.br |
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