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ARS Home » Plains Area » Clay Center, Nebraska » U.S. Meat Animal Research Center » Genetics and Animal Breeding » Research » Publications at this Location » Publication #389995
Research Project: Identifying Genomic Solutions to Improve Efficiency of Swine Production

Location: Genetics and Animal Breeding

Title: Sow posture and feeding activity monitoring in a farrowing pen using ground vibration

Author
item CODLING, JESSE - University Of Michigan
item DONG, YIWEN - Stanford University
item BONDE, AMELIE - Carnegie Mellon University
item BANNIS, ADEOLA - Carnegie Mellon University
item MACON, ASYA - University Of Nebraska
item Rohrer, Gary
item Miles, Jeremy
item SHARMA, S. RAJ - University Of Nebraska
item BROWN-BRANDL, TAMI - University Of Nebraska
item NOH, HAE YOUNG - Stanford University
item ZHANG, PEI - University Of Michigan

Submitted to: European Conference on Precision Agriculture Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 1/31/2022
Publication Date: N/A
Citation: N/A

Interpretive Summary:

Technical Abstract: 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 to monitor the structural vibration of a farrowing pen and 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. 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.