Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/28265
Title: Inteligência artificial aplicada à predição da temperatura superficial de frangos de corte
Other Titles: Artificial intelligence applied to the prediction of broiler’ surface temperature
Authors: Yanagi Junior, Tadayuki
Lacerda, Wilian Soares
Ferraz, Patrícia Ferreira Ponciano
Ferreira, Danton Diego
Miranda, Késia Oliveira da Silva
Pereira, Joelma Rezende Durão
Abreu, Lucas Henrique Pedrozo
Campos, Alessandro Torres
Keywords: Redes neurais artificiais
Termografia infravermelha
Avicultura
Artificial neural networks
Infrared thermography
Poultry production
Issue Date: 15-Dec-2017
Publisher: Universidade Federal de Lavras
Citation: CARVALHO, K. de A. Inteligência artificial aplicada à predição da temperatura superficial de frangos de corte. 2017. 47 p. Dissertação (Mestrado em Engenharia de Sistemas e Automação) – Universidade Federal de Lavras, Lavras, 2017.
Abstract: Broiler chickens in the initial growing stage do not have the thermoregulatory system fully developed and, if at this stage they are submitted to thermal discomfort conditions, the performance will be reduced. Surface temperature (Ts) is a physiological, non-invasive response in which the animal can be considered as a biosensor. Thus, the objective of this research was to develop a model based on artificial neural networks to predict the Ts of broiler chickens. To train and validate 100 neural networks, a database with 630 registers was randomly divided for training (70%), test (15%) and validation (15%). Subsequently, the best performing network was chosen. For the development of the RNAs, the input variables were the age of the birds (I) and the air temperature (Tar), and the output variable Ts. The developed RNAs adopted multilayer-perceptron (MLP) architecture with an input layer, a hidden layer and an output layer. The best network is able to predict with high reliability the Ts of young broiler chickens once a coefficient of determination (R2) of 0.9118 was obtained in the validation phase. With the characteristics of the best network, including the neuronal weights, it is possible to develop software that can be shipped in controllers, in order to control the thermal environment inside commercial broiler houses.
URI: http://repositorio.ufla.br/jspui/handle/1/28265
Appears in Collections:Engenharia de Sistemas e automação (Dissertações)



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