Digital twin perception and modeling method for feeding behavior of dairy cows

The digital twin of cows holds significant promise for advancing animal welfare and production efficiency. This paper aims to propose an architecture for digital twins of cows that covers their entire lifecycle. A digital twin solution has been developed that utilizes indoor positioning data and inertial measurement unit (IMU) data to construct a cow's digital shadow. As an example, the paper utilizes the classification of cow feeding and nonfeeding behaviors to study the digital twin perception and modeling methods. A custom-made collar integrated with utilized ultra-wideband (UWB) chips and inertial measurement units (IMUs) was utilized to collect real-time location and neck movement data from five healthy non-lactating Holstein cows. The collected data was transmitted via UWB signals to the positioning anchor and subsequently forwarded to a local server. To classify the feeding and non-feeding behaviors of the cows, three methods were employed: Support Vector Machines (SVM), K-Nearest Neighbor (KNN), and Long Short-Term Memory (LSTM). According to the experimental results, all three classification methods were effective, however, LSTM outperformed the others. Employing solely IMU data and implementing the LSTM, the precision of identifying bovine foraging behavior reached 91.05%, with concomitant precision and recall rates of 92.23 and 91.35%, respectively. Through an integration of the data from indoor position detection and IMU devices and the employment of LSTM, the accuracy of identification increased to 94.97%, with a precision rate of 99.99% and a recall rate of 93.86%. The trial of the digital twin solution demonstrated the rationality and technical feasibility of the digital twin architecture, which holds significant reference value for the development of animal digital twins in the animal husbandry industry.