Monitoring Multiple Behaviors in Beef Calves Raised in Cow-Calf Contact Systems Using a Machine Learning Approach.

ABSTRACT: The monitoring of pre-weaned calf behavior is crucial for ensuring health, welfare, and optimal growth. This study aimed to develop and validate a machine learning-based technique for the simultaneous monitoring of multiple behaviors in pre-weaned beef calves within a cow-calf contact (CCC) system using collar-mounted sensors integrating accelerometers and gyroscopes. Three complementary models were developed to classify feeding-related behaviors (natural suckling, feeding, rumination, and others), postural states (lying and standing), and coughing events. Sensor data, including tri-axial acceleration and tri-axial angular velocity, along with video recordings, were collected from 78 beef calves across two farms. The LightGBM algorithm was employed for behavior classification, and model pe

SUBMITTER: Kim SJ 

PROVIDER: S-EPMC11590895 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature