Predicting chick body mass by artificial intelligence-based models

Patricia Ferreira Ponciano Ferraz, Tadayuki Yanagi Junior, Yamid Fabián Hernández Julio, Jaqueline de Oliveira Castro, Richard Stephen Gates, Gregory Murad Reis, Alessandro Torres Campos

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)559-568
Number of pages10
JournalPesquisa Agropecuaria Brasileira
Issue number7
StatePublished - 2014


  • Animal welfare
  • Artificial neural network
  • Broiler
  • Modeling
  • Neuro-fuzzy network
  • Thermal comfort

ASJC Scopus subject areas

  • Animal Science and Zoology
  • Agronomy and Crop Science


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