| Registro completo |
| Provedor de dados: |
PAB
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| País: |
Brazil
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| Título: |
Predicting chick body mass by artificial intelligence-based models
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| Autores: |
Ferraz,Patricia Ferreira Ponciano
Yanagi Junior,Tadayuki
Hernández Julio,Yamid Fabián
Castro,Jaqueline de Oliveira
Gates,Richard Stephen
Reis,Gregory Murad
Campos,Alessandro Torres
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| Data: |
2014-07-01
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| Ano: |
2014
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| Palavras-chave: |
Animal welfare
Artificial neural network
Broiler
Modeling
Neuro-fuzzy network
Thermal comfort
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| Resumo: |
The objective of this work was to develop, validate, and compare 190 artificial intelligence-based models for predicting the body mass of chicks from 2 to 21 days of age subjected to different duration and intensities of thermal challenge. The experiment was conducted inside four climate-controlled wind tunnels using 210 chicks. A database containing 840 datasets (from 2 to 21-day-old chicks) - with the variables dry-bulb air temperature, duration of thermal stress (days), chick age (days), and the daily body mass of chicks - was used for network training, validation, and tests of models based on artificial neural networks (ANNs) and neuro-fuzzy networks (NFNs). The ANNs were most accurate in predicting the body mass of chicks from 2 to 21 days of age after they were subjected to the input variables, and they showed an R² of 0.9993 and a standard error of 4.62 g. The ANNs enable the simulation of different scenarios, which can assist in managerial decision-making, and they can be embedded in the heating control systems.
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| Tipo: |
Info:eu-repo/semantics/article
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| Idioma: |
Inglês
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| Identificador: |
http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-204X2014000700559
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| Editor: |
Embrapa Secretaria de Pesquisa e Desenvolvimento
Pesquisa Agropecuária Brasileira
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| Relação: |
10.1590/S0100-204X2014000700009
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| Formato: |
text/html
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| Fonte: |
Pesquisa Agropecuária Brasileira v.49 n.7 2014
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| Direitos: |
info:eu-repo/semantics/openAccess
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