Current Trends in Artificial Intelligence and Bovine Mastitis Research: A Bibliometric Review Approach

Area of Science:

• Veterinary Science
• Computer Science
• Data Science

Background:

• Bovine mastitis significantly impacts dairy herd profitability, necessitating accurate diagnostic methods.
• Artificial intelligence (AI) models offer potential for improved mastitis classification, identification, prediction, and diagnosis.

Purpose of the Study:

• To conduct a bibliometric review of artificial intelligence (AI) and bovine mastitis research.
• To analyze trends and identify key themes in Scopus-indexed publications from 2011 to 2021.

Main Methods:

• Bibliometric analysis of 62 Scopus-indexed documents.
• Identification of key terms, prominent researchers, publications, themes, and keyword clusters.
• Trend analysis of AI and bovine mastitis research from 2011 to 2021.

Main Results:

• "Mastitis" and "machine learning" were the most cited terms, with increasing trends from 2018-2021.
• "Sensors" and "mastitis detection" emerged as relevant terms.
• The United States led in publications and collaborations; research interest surged from 2016-2021.

Conclusions:

• AI research for bovine mastitis is growing, with a notable increase in publications.
• Few studies have specifically utilized AI for bovine mastitis detection, primarily using artificial neural networks.
• Significant potential exists for further research and development of AI applications in bovine mastitis detection.