Role of Machine Learning in Livestock Health Monitoring System: A Systematic Literature Review

Muhammad Mohsin Raza; Rabia Tehseen; Uzma Omer; Muhammad Qasim; Usman Aamer; Ramsha Saeed; Muhammad Farrukh Khan

Authors

Muhammad Mohsin Raza Department of Computer Science, University of Central Punjab, Lahore, Pakistan
Rabia Tehseen Department of Computer Science, University of Central Punjab, Lahore, Pakistan
Uzma Omer Department of Computer Science, University of Education, Lahore, Pakistan
Muhammad Qasim Department of Computer Science, University of Central Punjab, Lahore, Pakistan
Usman Aamer Department of Computer Science, University of Central Punjab, Lahore, Pakistan
Ramsha Saeed Department of Computer Science, University of Central Punjab, Lahore, Pakistan
Muhammad Farrukh Khan Department of Computing, NASTP Institute of Information Technology, Lahore, Pakistan

Keywords:

Machine Learning, IoT, Livestock Health System, Precision Livestock, Livestock Monitoring, Animal Welfare, Precision Farming, Livestock diseases

Abstract

Machine Learning (ML) can significantly enhance livestock management in various ways by providing real-time insights into animal health, behavior, and well-being. Livestock production, monitoring, and management can be revolutionized by using ML techniques. This study presents a comprehensive review of the literature regarding IoT devices used for monitoring cattle health, key characteristics of these devices, wearable technology used, sensors, and ML algorithms. In order to complete the review, a thorough examination and synthesis of the research articles published in reputable research venues between 2018 and 2023 are conducted. The findings revealed that pressure and pulse-rate sensors are the most often utilized types for recording the health status of animals experiencing health issues.

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