The role and diverse applications of machine learning in genetics, breeding, and biotechnology of livestock and poultry

Document Type : Research Paper

Authors

1 Professor of Animal Science Department, Shahid Bahonar University of Kerman

2 Department of Biology, Faculty of Science, Shahid Bahonar University of Kerman, Kerman, Iran

3 Animal Science Department, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran.

4 Department of Animal Science, Bila Tserkva National Agrarian University, Bila Tserkva, Ukraine.

5 Department of Technologies of Livestock Production and processing, Higher Educational Institution “Podillia State University”, Ukraine.

6 Poltava State Agrarian University, Ukraine.

7 National University of Life and Environmental Sciences of Ukraine, Ukraine.

10.22103/jab.2025.24662.1644

Abstract

Objective
Machine learning is a subset of artificial intelligence that is uniquely suited to address challenges in the fields of genetics, breeding, and biotechnology of livestock and poultry. By using algorithms that can learn patterns from data, machine learning enables accurate predictions, automated decision-making, and innovative solutions to complex problems in animal science. Unlike traditional statistical methods, which often assume linearity and independence among variables, machine learning is able to capture nonlinear relationships and interactions between genomic, environmental, and phenotypic factors. Therefore, the purpose of this study was to review the common types of machine learning algorithms used in livestock and poultry breeding, to outline their advantages and disadvantages, and to provide practical examples for these algorithms in the fields of genetics, breeding, and biotechnology of livestock and poultry.
Materials and Methods
In this study, by reviewing relevant databases and journals, studies related to machine learning in the field of genetics and breeding and biotechnology of livestock and poultry were searched using keywords. These studies were evaluated based on their design, methodology, results and relevance, and the main findings and concepts were extracted from them.
Results
The results showed that machine learning methods significantly outperform conventional methods. So that machine learning methods improve prediction accuracy and have smaller mean square error (MSE) and mean absolute error (MAE) in all scenarios. The findings also show the potential of combining classical bioinformatics methods with machine learning techniques to improve genomic prediction in the future. The results suggest machine learning algorithms as a promising tool to improve decision-making for livestock farmers. Machine learning analysis improves monitoring methods and allows livestock farmers to identify animals that are likely to have problems in the future.
Conclusions
This study shows that the use of machine learning methods in the field of genetics, breeding, and biotechnology of livestock and poultry is increasing, and with this increase, the quality of machine learning methods used is also improving. Therefore, machine learning can play an important and prominent role in the sustainable development of livestock farming and provide benefits such as increased productivity in this field. Therefore, this study recommends that the use of machine learning methods and algorithms be promoted among the activists in the field of genetics, breeding, and biotechnology of livestock and poultry to identify and predict problems earlier and more accurately and prevent problems and economic losses.

Keywords


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