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.
Cite
CITATION STYLE
Raza, M. M., Tehseen, R., Omer, U., Qasim, M., Aamer, U., Saeed, R., & Khan, M. F. (2025). Role of Machine Learning in Livestock Health Monitoring System: A Systematic Literature Review. International Journal of Agriculture & Sustainable Development, 986–1005. https://doi.org/10.33411/ijist/2025729861005
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.