With the development of the Internet of Things (IoT), digital technology has been adopted on livestock farms. In this study, a system for monitoring the health status of dairy cows is proposed, utilizing multi-type sensor fusion and IoT technology. The system uses physiological and behavioral data obtained from a tail sensor attached to the cows to establish prenatal and estrus prediction models. Additionally, environmental sensors installed in the barn monitor parameters such as temperature, humidity, carbon dioxide concentration, and organic gas concentration to automatically regulate the barn’s fan and sprinkler based on preset threshold values. The system also monitors the health and tail behavior of the cows and predicts their calving and estrus times based on the collected data. Experimental results demonstrate that the system exhibits high accuracy and reliability in monitoring the health of dairy cows.

We propose a model for demand estimation in multi-agent, differentiated product settings and present an estimation algorithm that uses reversible jump MCMC techniques to classify agents' types. Our model extends the popular setup in Berry, Levinsohn and Pakes (1995) to allow for the data-driven classification of agents' types using agent-level data. We focus on applications involving data on agents' ranking over alternatives, and present theoretical conditions that establish the identifiability of the model and uni-modality of the likelihood/posterior. Results on both real and simulated data provide support for the scalability of our approach.