Abstract: With the development of dairy cattle breeding technology,advancements in feed science,and the progress of genetic improvement,the milk yield per cow has continuously increased.Many countries have seen their dairy production reach historical highs.Meanwhile,consumer demand for dairy products has also changed,with a tendency to choose higher-quality products.Rumination is a unique behavior of ruminant animals and is one of the important indicators reflecting their health and production performance.Rumination helps ruminants better digest and absorb nutrients from their food,improving both the quantity and quality of milk.Additionally,monitoring the rumination behavior of ruminants aids producers in timely understanding their health status and production levels,allowing them to adjust breeding strategies promptly and reduce economic losses.In the context of increasingly large-scale and intensive farming models,the time and economic costs of manual monitoring are rising.With technological advancements,intelligent monitoring methods have provided technical support for the automatic monitoring of rumination,leading to the emergence of more intelligent monitoring solutions.In particular,the continuous updates and iterations of hardware technology and software algorithms have begun to showcase machine learning algorithms in both contact-based and non-contact-based monitoring methods for dairy cow rumination.The authors review how these typical factors influence the rumination behavior of dairy cows from multiple aspects,it also organize common contact-based methods for monitoring rumination behavior,as well as non-contact intelligent monitoring methods.The paper elaborates on the algorithm principles and application effects of these relevant methods,listing some common applications of rumination monitoring methods at present and categorizing them.Finally,it discusses the existing issues in intelligent monitoring of rumination behavior and prospects for its future development,aiming to provide references for related research.

Key words: dairy cows; rumination behavior; rumination monitoring; intelligent monitoring