Development of individual models for predicting cow milk production for real-time monitoring

Daily milk yield serves as a physiological indicator in dairy cows and is a primary target for prediction and realtime monitoring in smart livestock farming. This study attempted to develop an individual model for predicting daily milk yield and applied it to monitor the health status of dairy cows by designing a real-time monitoring algorithm. A total of 580 datasets were used for model development after data preprocessing and screening, which were subsequently used to develop the model by modifying the existing models based on nonlinear regression analysis. The developed model was then applied to short-term real-time monitoring of abnormal daily milk yields. The optimal model was able to predict the daily milk yield, with an R2 value of 0.875 and a root mean squared error of 2.192. Real-time monitoring was designed to detect abnormal daily milk yields by collectively considering a 90% confidence interval and the difference between predicted values and expected trends. This study is the first to design a monitoring algorithm for daily milk yield from dairy cows based on an individual model capable of predicting the daily milk yield. This study expects that a platform will be necessary for highly efficient smart livestock farming, enabling high productivity with minimal inputs.