Estimation of milk yield based on udder measures of Pelibuey sheep using artificial neural networks.

ABSTRACT: 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

SUBMITTER: Angeles-Hernandez JC 

PROVIDER: S-EPMC9151640 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature