Predicting chick body mass by artificial intelligence-based models


Autoria(s): Ferraz,Patricia Ferreira Ponciano; Yanagi Junior,Tadayuki; Hernández Julio,Yamid Fabián; Castro,Jaqueline de Oliveira; Gates,Richard Stephen; Reis,Gregory Murad; Campos,Alessandro Torres
Data(s)

01/07/2014

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.

Formato

text/html

Identificador

http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-204X2014000700559

Idioma(s)

en

Publicador

Embrapa Informação Tecnológica

Pesquisa Agropecuária Brasileira

Fonte

Pesquisa Agropecuária Brasileira v.49 n.7 2014

Palavras-Chave #animal welfare #artificial neural network #broiler #modeling #neuro-fuzzy network #thermal comfort
Tipo

journal article