Predicting chick body mass by artificial intelligence-based models

Abstract

-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.Index terms: animal welfare, artificial neural network, broiler, modeling, neuro-fuzzy network, thermal comfort. Predição da massa corporal de pintinhos por meio de modelos baseados em inteligência artificialResumo -O objetivo deste trabalho foi desenvolver, validar e comparar 190 modelos baseados em inteligência artificial, para predizer a massa corporal de pintinhos de 2 a 21 dias de vida, submetidos a diferentes períodos e intensidades de estresse térmico. O experimento foi realizado com 210 pintinhos, em quatro túneis de vento climatizados. Um banco de dados com 840 conjuntos de dados (de aves de 2 a 21 dias) -com as variáveis temperatura de bulbo seco do ar, duração do estresse térmico (dias), idade das aves (dias) e a massa corporal diária dos pintinhos -foi utilizado para treinamento de rede, validação e testes dos modelos baseados em redes neurais artificiais (RNA) e redes "neuro-fuzzy" (RNF). As RNA mostraram-se mais precisas para se predizer a massa corporal de pintinhos de 2 a 21 dias de idade, submetidos às variáveis de entrada, e apresentaram R² de 0,9993 e erro-padrão de 4,62 g. As RNA propiciam a simulação de diversos cenários, que podem auxiliar na tomada de decisões em relação ao manejo, e podem ser incorporadas nos sistemas de controle de aquecimento.Termos para indexação: bem estar animal, redes neurais artificiais, frango, modelagem, redes neurais difusas, conforto térmico.

Artificial Neural Networks for Prediction of Physiological and Productive Variables of Broilers

“…As the number of neurons in the hidden layer increases, performance improves and reaches satisfactory levels; however, if the number of neurons in the hidden layer is excessive, performance can be compromised, because many weights and the bias of neurons could have values equal to zero and would increase the processing of output value calculations (spatial and temporal complexity). In this case, the methodology proposed by Ferraz et al (2014) recommends using the model with the best performance, using the highest coefficient of determination (R²) and the lowest MSE. Thus, these values were obtained with values of intermediate neurons.…”

“…When using ANN to predict the body mass of broilers, Ferraz et al (2014) found mean values for absolute deviation, standard deviation, and percentage error of 3.3, 2.3, and 1.2%, respectively. For the ANN developed, considering the same statistical analyses, it can be verified that the values are modified according to the output variable; however, the mean values found in this study were close to the values published by the cited authors.…”

“…Thermal stress was applied at four levels of duration (1, 2, 3, and 4 days) in the first four days that make up the second week (8th, 9th, 10th, and 11th days of life); shortly after this period, the temperatures returned to the thermal comfort zone (30ºC). The experimental stage submitted to a temperature of 30°C was considered the control (comfort), and the others were maintained in order to provide thermal challenges both by low (24ºC and 27ºC) and high (33ºC) temperatures (Curtis, 1983;Cassuce et al, 2013).…”

“…Three feedforward layers (input, hidden layer, and output layer) and supervised training were employed with the Levenberg-Marquardt backpropagation training algorithm, which is considered the fastest method for networking (Barbosa et al, 2005). MSE was used for the performance function of the model, in which, for the output of the neuron, the sigmoidal tangent activation function was selected (Ferraz et al, 2014;Oliveira et al, 2015).…”

“…Therefore, the importance of monitoring the thermal stress of broilers and the influence on behavioral parameters, physiological responses, and productive performance has been verified. Thus, through information generated by intelligent systems, the producer will be able to control his business more appropriately (Pandorfi et al, 2012;Ferraz et al, 2014).…”