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<p>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.</p>
</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.

Application of Machine Learning in Estimating Milk Yield According to the Phenotypic and Pedigree Data of Holstein-Friesian Cattle in Serbia

Contemporary Agriculture

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

This paper presents a deep neural network (DNN) approach designed to estimate the milk yield of Holstein-Friesian cattle. The DNN comprised stacked dense...

Application of Machine Learning in Estimating Milk Yield According to the Phenotypic and Pedigree Data of Holstein-Friesian Cattle in Serbia

Pubblicato online: 22 dic 2023

Pagine: 181 - 187

Ricevuto: 30 set 2023

Accettato: 23 nov 2023

DOI: https://doi.org/10.2478/contagri-2023-0024

Parole chiave
assessment of milk yield, machine learning, cattle, prediction of breeding values

© 2023 Laslo Tarjan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Application of Machine Learning in Estimating Milk Yield According to the Phenotypic and Pedigree Data of Holstein-Friesian Cattle in Serbia

Pubblicato online: 22 dic 2023

Pagine: 181 - 187

Ricevuto: 30 set 2023

Accettato: 23 nov 2023

DOI: https://doi.org/10.2478/contagri-2023-0024

Parole chiaveassessment of milk yield, machine learning, cattle, prediction of breeding values

© 2023 Laslo Tarjan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Parole chiave
assessment of milk yield, machine learning, cattle, prediction of breeding values