Predicting chick body mass by artificial intelligence‑based models

• Autor(a) principal: Ferraz, Patricia Ferreira Ponciano
• Data de Publicação: 2014
• Outros Autores: Yanagi Junior, Tadayuki, Hernández Julio, Yamid Fabián, Castro, Jaqueline de Oliveira, Gates, Richard Stephen, Reis, Gregory Murad, Campos, Alessandro Torres
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UFLA
• Texto Completo: http://repositorio.ufla.br/jspui/handle/1/11627
• topic: Animal welfare; Artificial neural network; Broiler; Modeling; Neuro‑fuzzy network; Thermal comfort; Bem estar animal; Redes neurais artificiais; Frango; Modelagem; Redes neurais difusas; Conforto térmico

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.