Prediction and optimization of slaughter weight in meat-type quails using artificial neural network modeling

Abstract

Carcass yield of meat-type quails is strongly correlated with the weight of the birds at slaughter (slaughter weight [ SW ]; body weight at 45 D of age). Moreover, prediction of superior animals for SW at the earlier stages of the rearing period is favorable for producers. Therefore, the aim of the present study was to predict and optimize SW of Japanese quails based on their early growth performances, sex, and egg weight as predictors through artificial neural network ( ANN ) modeling. To construct the ANN model a feed-forward multilayer perceptron neural network structure was used. Moreover, sensitivity analysis was used to arrange the predictors in the ANN model(s) according to their predictive importance too. In addition, the optimization process was conducted to determine the optimum values for the input variables to yield maximum SW. The best-fitted network on input data to predict SW in Japanese quails was determined with 7 neurons in the input layer, 11 neurons in the hidden layer, and one neuron in the output layer. The coefficient of determination (R 2 ) was 0.9404, 0.9359, and 0.9223 for training, validation, and testing phases, respectively. For the corresponding phases, SEM were also 51.8854, 52.2764, and 55.2572, respectively. According to sensitivity analysis, the most important input variable for prediction of SW was body weight at 20 D of age ( BW20 ), whereas the less important input variables were weight of the birds at hatch and body weight at 5 D of age. The results of the neural network optimization indicated that all the input variables, except for BW20, were very similar but slightly higher than mean values (μ for each input variable). The results of this study suggest that the ANN provides a practical approach to predict the final body weight (SW) of Japanese quails based on early performances. Moreover, phenotypic selection for higher values of early growth traits did not ensure the achievement of maximum SW, except for BW20.

Scientometric evaluation of 100-year history of Poultry Science (1921–2020)

“…Entering the big-data era and utilizing the high-throughput technologies in biological sciences widely influenced the contribution of mathematical models to extract biological concepts from the raw data ( Cogburn et al, 2007 ). Developing mathematical methods and employing artificial intelligence in the poultry science have become undeniable ( Jahan et al, 2020 ). It seems that modeling remains one of the most attractive research areas for scientists at least in the upcoming future decades.…”

Investigation of egg production curve in ostrich using nonlinear functions

“…, 2000 ; Grossman and Koops, 2001 ; Savegnago et al . , 2012 ), predicting future records and trends ( Adams and Bell, 1980 ; Brand et al, 2012 ; Faraji-Arough et al, 2019 ; Jahan et al, 2020 ), evaluating progress through the selection process ( Savegnago et al . , 2012 ), and also estimating deviation from the expected production curve ( Alvarez and Hocking, 2007 ; Johnston and Gous, 2007 ; Faraji-Arough et al, 2018 ).…”