A COMPREHENSIVE BIBLIOMETRIC ANALYSIS OF MACHINE LEARNINGAPPLICATIONS IN CATTLE BREEDING AND PRODUCTION

As artificial intelligence applications have developed, machine learning, deep learning, and

neural networks, whose algorithms are powerful and flexible predictive modeling tools, have

started to be widely used in animal husbandry, especially in cattle production and breeding. In

our study, bibliometric analyses of machine learning applications in cattle breeds were

conducted using the keywords "machine learning in cattle" in the Web of Science (WOS)

(https://www.webofscience.com) and Scopus (https://www.scopus.com/search) databases. The

analysis showed that from 2001 to 2024, 586 publications were published in WOS and 852

were published in Scopus on this topic. The majority of the publications were published in the

areas of agriculture, dairy animal science, and veterinary science. A large part of the research

was carried out in the United States and China. Machine learning has been widely used in cattle

breeding for many purposes such as disease screening, keeping track of yields, improving farm

conditions, identifying species, identifying markers, and creating security protocols. Our work

highlights the importance and potential uses of artificial intelligence in cattle breeding and

development, especially in improving farm conditions, using bibliometric studies.

Keywords: Machine Learning, Cattle, Deep Learning, Computer Vision, Precision

Livestock Farming