Estimation of milk yield based on udder measures of Pelibuey sheep using artificial neural networks
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Estimation of milk yield based on udder measures of Pelibuey sheep using artificial neural networks. / Angeles-Hernandez, J. C.; Castro-Espinoza, F. A.; Peláez-Acero, A.; Salinas-Martinez, J. A.; Chay-Canul, A. J.; Vargas-Bello-Pérez, E.
I: Scientific Reports, Bind 12, 9009, 2022.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Estimation of milk yield based on udder measures of Pelibuey sheep using artificial neural networks
AU - Angeles-Hernandez, J. C.
AU - Castro-Espinoza, F. A.
AU - Peláez-Acero, A.
AU - Salinas-Martinez, J. A.
AU - Chay-Canul, A. J.
AU - Vargas-Bello-Pérez, E.
N1 - Publisher Copyright: © 2022, The Author(s).
PY - 2022
Y1 - 2022
N2 - Udder measures have been used to assess milk yield of sheep through classical methods of estimation. Artificial neural networks (ANN) can deal with complex non-linear relationships between input and output variables. In the current study, ANN were applied to udder measures from Pelibuey ewes to estimate their milk yield and this was compared with linear regression. A total of 357 milk yield records with its corresponding udder measures were used. A supervised learning was used to train and teach the network using a two-layer ANN with seven hidden structures. The globally convergent algorithm based on the resilient backpropagation was used to calculate ANN. Goodness of fit was evaluated using the mean square prediction error (MSPE), root MSPE (RMSPE), correlation coefficient (r), Bayesian’s Information Criterion (BIC), Akaike’s Information Criterion (AIC) and accuracy. The 15–15 ANN architecture showed that the best predictive milk yield performance achieved an accuracy of 97.9% and the highest values of r2 (0.93), and the lowest values of MSPE (0.0023), RMSPE (0.04), AIC (− 2088.81) and BIC (− 2069.56). The study revealed that ANN is a powerful tool to estimate milk yield when udder measures are used as input variables and showed better goodness of fit in comparison with classical regression methods.
AB - Udder measures have been used to assess milk yield of sheep through classical methods of estimation. Artificial neural networks (ANN) can deal with complex non-linear relationships between input and output variables. In the current study, ANN were applied to udder measures from Pelibuey ewes to estimate their milk yield and this was compared with linear regression. A total of 357 milk yield records with its corresponding udder measures were used. A supervised learning was used to train and teach the network using a two-layer ANN with seven hidden structures. The globally convergent algorithm based on the resilient backpropagation was used to calculate ANN. Goodness of fit was evaluated using the mean square prediction error (MSPE), root MSPE (RMSPE), correlation coefficient (r), Bayesian’s Information Criterion (BIC), Akaike’s Information Criterion (AIC) and accuracy. The 15–15 ANN architecture showed that the best predictive milk yield performance achieved an accuracy of 97.9% and the highest values of r2 (0.93), and the lowest values of MSPE (0.0023), RMSPE (0.04), AIC (− 2088.81) and BIC (− 2069.56). The study revealed that ANN is a powerful tool to estimate milk yield when udder measures are used as input variables and showed better goodness of fit in comparison with classical regression methods.
U2 - 10.1038/s41598-022-12868-0
DO - 10.1038/s41598-022-12868-0
M3 - Journal article
C2 - 35637273
AN - SCOPUS:85131010368
VL - 12
JO - Scientific Reports
JF - Scientific Reports
SN - 2045-2322
M1 - 9009
ER -
ID: 313494374