Sow Posture and Feeding Activity Monitoring in a Farrowing Pen Using Ground Vibration

Automated monitoring of sow welfare and behaviors is a crucial tool in precision swine farming, giving farmers access to continuous streams of sow health information. Monitoring the activity of the sows helps farmers detect stress, sickness and signs of farrowing, which enables the farmers to provide timely care. Prior work in swine monitoring frequently uses video cameras, which have lighting and large storage and processing requirements. Alternatively, other work has used wearable sensors, which have limited longevity due to durability and battery requirements and suffer from scalability challenges due to the need for individual sensors worn by each sow. The objective of the study was to determine the effectiveness of geophone sensors mounted under the floor that measure the structural vibration of a farrowing pen to determine posture changes and animal feeding activity. A total of 6 farrowing/lactating sows and litters have been used in these studies. The data were collected from a minimum of 3 days before farrowing to approximately 25 days post-farrow. Up to five geophones were used for activity classification. Machine learning classification methods are used to detect the position and feeding activity of the sow and her piglets, including tree classifiers and principal component analysis. Accuracies of over 95% were achieved in sow posture and feeding activity classification, indicating the potential of monitoring ground vibration as a source of health information.