Evaluation of a novel computer vision-based livestock monitoring system to identify and track specific behaviors of individual nursery pigs within a group-housed environment. Issue 3 (16th June 2022)

• Record Type: Journal Article
• Authors: Schmidt, Ty B; Lancaster, Jessica M; Psota, Eric; Mote, Benny E; Hulbert, Lindsey E; Holliday, Aaron; Woiwode, Ruth; Pérez, Lance C
• Is Part Of: Translational animal science. Volume 6:Issue 3(2022)
• Publication Date: 2022-06-16
• Subjects: animal behavior -- individual identification -- Kinect v2 -- multiple-object tracking -- precision livestock technology; Livestock -- Periodicals; Livestock; Periodicals; 636.005
• DOI: 10.1093/tas/txac082
• Languages: English
• ISSNs: 2573-2102

Abstract

Abstract: Animal behavior is indicative of health status and changes in behavior can indicate health issues (i.e., illness, stress, or injury). Currently, human observation (HO) is the only method for detecting behavior changes that may indicate problems in group-housed pigs. While HO is effective, limitations exist. Limitations include HO being time consuming, HO obfuscates natural behaviors, and it is not possible to maintain continuous HO. To address these limitations, a computer vision platform (NU track) was developed to identify (ID) and continuously monitor specific behaviors of group-housed pigs on an individual basis. The objectives of this study were to evaluate the capabilities of the NU track s ystem and evaluate changes in behavior patterns over time of group-housed nursery pigs. The NU track system was installed above four nursery pens to monitor the behavior of 28 newly weaned pigs during a 42-d nursery period. Pigs were stratified by sex, litter, and randomly assigned to one of two pens (14 pigs/pen) for the first 22 d. On day 23, pigs were split into four pens (7 pigs/pen). To evaluate the NU track system's capabilities, 800 video frames containing 11, 200 individual observations were randomly selected across the nursery period. Each frame was visually evaluated to verify the NU track system's accuracy for ID and classification of behavior. The NU track system achieved an overall accuracy for ID of 95.6%. This accuracy for ID was 93.5% during the first 22 d and increased ( P < 0.001) to 98.2% for the final 20 d. Of the ID errors, 72.2% were due to mislabeled ID and 27.8% were due to loss of ID. The NU track system classified lying, standing, walking, at the feeder (ATF), and at the waterer (ATW) behaviors accurately at a rate of 98.7%, 89.7%, 88.5%, 95.6%, and 79.9%, respectively. Behavior data indicated that the time budget for lying, standing, and walking in nursery pigs was 77.7% ± 1.6%, 8.5% ± 1.1%, and 2.9% ± 0.4%, respectively. In addition, behavior data indicated that nursery pigs spent 9.9% ± 1.7% and 1.0% ± 0.3% time ATF and ATW, respectively. Results suggest that the NU track system can detect, identify, maintain ID, and classify specific behavior of group-housed nursery pigs for the duration of the 42-d nursery period. Overall, results suggest that, with continued research, the NU track system may provide a viable real-time precision livestock tool with the ability to assist producers in monitoring behaviors and potential changes in the behavior of group-housed pigs.