Abstract

This paper presents a deep neural network (DNN) approach designed to estimate the milk yield of Holstein-Friesian cattle. The DNN comprised stacked dense (fully connected) layers, each hidden layer followed by a dropout layer. Various configurations of the DNN were tested, incorporating 2 and 3 hidden layers containing 8 to 54 neurons. The experiment involved testing the DNN with different activation functions such as the sigmoid, tanh, and rectified linear unit (ReLU). The dropout rates ranging from 0 to 0.3 were employed, with the output layer using a linear activation function. The DNN models were trained using the Adam, SGD, and RMSprop optimizers, with the root mean square error serving as the loss metric. The training dataset comprised information from a unique database containing records of dairy cows in the Republic of Serbia, totaling 3,406 cows. The input parameters (a total of 27) for the DNN included breeding and milk yield data from the cow’s mother, as well as the father’s ID, whereas the output parameters (a total of 8) consisted of milk yield parameters (a total of 3) and breeding parameters of the cow (a total of 5). Training iterations were conducted using a batch size of 8 over 500, 1000, and 5000 epochs.

Details

Title
Application of Machine Learning in Estimating Milk Yield According to the Phenotypic and Pedigree Data of Holstein-Friesian Cattle in Serbia
Author
Tarjan, Laslo 1   VIAFID ORCID Logo  ; Šenk, Ivana 1   VIAFID ORCID Logo  ; Pracner, Doni 2   VIAFID ORCID Logo  ; Štrbac, Ljuba 3   VIAFID ORCID Logo  ; Šaran, Momčilo 3   VIAFID ORCID Logo  ; Ivković, Mirko 3   VIAFID ORCID Logo  ; Dedović, Nebojša 3   VIAFID ORCID Logo 

 University of Novi Sad, Faculty of Technical Sciences, Trg Dositeja Obradovića 6, Novi Sad, Serbia 
 University of Novi Sad, Faculty of Sciences, Trg Dositeja Obradovića 3, Novi Sad, Serbia 
 University of Novi Sad, Faculty of Agriculture, Trg Dositeja Obradovića 8, Novi Sad, Serbia 
Pages
181-187
Publication year
2023
Publication date
2023
Publisher
De Gruyter Poland
e-ISSN
24664774
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3158936286
Copyright
© 2023. This work is published under http://creativecommons.org/licenses/by-nc-nd/3.0 (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.