TY - JOUR
T1 - Predicting chick body mass by artificial intelligence-based models
AU - Ferraz, Patricia Ferreira Ponciano
AU - Junior, Tadayuki Yanagi
AU - Julio, Yamid Fabián Hernández
AU - de Oliveira Castro, Jaqueline
AU - Gates, Richard Stephen
AU - Reis, Gregory Murad
AU - Campos, Alessandro Torres
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
KW - Animal welfare
KW - Artificial neural network
KW - Broiler
KW - Modeling
KW - Neuro-fuzzy network
KW - Thermal comfort
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U2 - 10.1590/S0100-204X2014000700009
DO - 10.1590/S0100-204X2014000700009
M3 - Article
AN - SCOPUS:84911372100
SN - 0100-204X
VL - 49
SP - 559
EP - 568
JO - Pesquisa Agropecuaria Brasileira
JF - Pesquisa Agropecuaria Brasileira
IS - 7
ER -