Machine Learning in Livestock Management: A Systematic Exploration of Techniques and Outcomes

Muhammad Qasim ()
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Muhammad Qasim: Department of Computer Science, University of Central Punjab, Lahore,53400, Pakistan

International Journal of Innovations in Science & Technology, 2024, vol. 6, issue 4, 1968-1984

Abstract: This Systematic Literature Review (SLR)examines the growing field of leveraging Machine Learning (ML) to improve livestock productivity. Through a meticulous analysis of peer-reviewed articles, the study categorizes research into key domains such as disease detection, feed optimization, and reproductive management. Various ML algorithms, including supervised, unsupervised, and reinforcement learning, are evaluated for their efficacy in enhancing herd health and management. The review also addressesthe role of diverse data sources, such as sensor technologies and electronic health records, and discusses the socio-economic and ethical implications of ML adoption in livestock farming. Insights into scalability, economic viability, and future research directions contribute to a comprehensive understanding of the current background and pave the way for sustainable and technologically advanced livestock management practices. This review serves as a valuable resource for researchers, practitioners, and policymakers in shaping the future of precision agriculture in improving livestock productivity.

Keywords: Livestock; Animal; Improvement; Productivity; Machine Learning; Deep Learning (search for similar items in EconPapers)
Date: 2024
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