Authors
1
Assistant prof., Dept. of Animal Sciences, Ahar Faculty of Agriculture and Natural Resources, University of Tabriz, Ahar, Iran
2
Associated prof.,, Dept. of Animal Sciences, Ahar Faculty of Agriculture and Natural Resources, University of Tabriz, Ahar, Iran
3
Ahar Faculty of Agriculture and Natural Resources, University of Tabriz, Ahar, Iran
4
دانش آموخته کارشناسی ارشد، گروه علوم دامی، دانشکده کشاورزی و منابع طبیعی اهر، دانشگاه تبریز
10.22069/ejrr.2024.21843.1921
Abstract
Background and Objectives: In this study artificial neural network (ANN) used to predict milk test day records at 4th, 5th, 10th months of lactation duration and 305-day milk yield in Holstein dairy cows.
Materials and Methods: Primary data source was consisting of 274025 milk production records of 7201 primiparuse to fourth birth Holstein cows, from two herd. Final source of data obtained from milk production records was consist of 87980 monthly milk test day records in 8798 rows which each row contains number of animal, herd, age, lactation, month of production, first to tenth monthly milk production records and 305-day milk yield. From the total of data, 50% was considered for neural network training, 20% for validation and 30% for testing. A multilayer perceptron (MLP) network with back propagation of error learning mechanism (BP) was used through different artificial neural network (ANN) structures to predict milk production. In order to optimize artificial neural network (ANN) structure three activation functions (hyperbolic tangent axon, sigmoid axon or linear hyperbolic tangent axon) and three back propagation algorithms viz. momentum, conjugate gradient (CG) and Leven-berg–Marquardt (LM) Training algorithms used in the hidden layer as well as in the output layer. Coefficient of determination, root of mean square error and mean absolute error were used to compare algorithms.
Results: In prediction of milk production of 4th and 5th monthly test day records, LM algorithm with sigmoid axon activation functions and LM Training algorithm, with hyperbolic tangent Axon functions had the best performance between network structures respectively. In these net work structures R2 were highest (0.725 and 0.642 respectively), RMSEs were lowest (4.785 and 5.345 respectively) and MAEs were lowest (3.715and 4.057 respectively). In prediction of 10th monthly test day milk production through three or four monthly test day records, obtained from the same lactation period, none of the structures had ability to predict milk production successfully. In prediction of 305-day milk yield, LM algorithm and hyperbolic tangent activation function had the best prediction through 3 test day records and R2, RMSE and MAE as performance criteria were 0.799, 984.14 and 790.21 respectively. Also the same structure of the network had the best performance to predict 305-day milk yield through four or five initial test day records and performance criteria, Coefficient of determination, root of mean square error and mean absolute error were 0.856, 850.98 and 653.33 respectively, in ANN with four test day record as input variables and 0.904, 706.59 and 548.69 respectively, in ANN with five test day record as input variables, respectively.
Conclusion: The artificial neural network designed in this study was able to predict the milk production of animals in the fourth month of lactation with a correlation coefficient of 0.84. On the other hand, the designed neural network was able to predict the total milk production of the animal in a lactation period of 305 days with appropriate accuracy. So that the correlation coefficients in using the first three, four and five monthly records of livestock for prediction were 0.89, 0.92 and 0.95 respectively.
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