Automated Tracking and Behavior Quantification of Laying Hens Using 3D Computer Vision and Radio Frequency Identification Technologies Nakarmi, Akash Tang, Lie Xin, Hongwei
dc.contributor.department Agricultural and Biosystems Engineering 2018-02-15T03:18:10.000 2020-06-29T22:41:16Z 2020-06-29T22:41:16Z Wed Jan 01 00:00:00 UTC 2014 2014-10-21 2014-01-01
dc.description.abstract <p>Housing design and management schemes (e.g., bird stocking density) in egg production can impact hens’ ability to perform natural behaviors and production economic efficiency. It is therefore of socio-economic importance to quantify the effects of such schemes on laying-hen behaviors, which may in turn have implications on the animals’ well-being. Video recording and manual video analysis is the most common approach used to track and register laying-hen behaviors. However, such manual video analyses are labor intensive and are prone to human error, and the number of target objects that can be tracked simultaneously is small. In this study, we developed a novel method for automated quantification of certain behaviors of individual laying hens in a group-housed setting (1.2 m × 1.2 m pen), such as locomotion, perching, feeding, drinking, and nesting. Image processing techniques were employed on top-view images captured with a state-of-the-art time-of-flight (ToF) of light based 3D vision camera for identification as well as tracking of individual birds in the group with support from a passive radio-frequency identification (RFID) system. Each hen was tagged with a unique RFID transponder attached to the lower part of her leg. An RFID sensor grid consisting of 20 antennas installed underneath the pen floor was used as a recovery system in situations where the imaging system failed to maintain identities of the birds. Spatial as well as temporal data were used to extract the aforementioned behaviors of each bird. To test the performance of the tracking system, we examined the effects of two stocking densities (2880 vs. 1440 cm2 hen-1) and two perching spaces (24.4 vs. 12.2 cm of perch per hen) on bird behaviors, corresponding to five hens vs. ten hens, respectively, in the 1.2 m × 1.2 m pen. The system was able to discern the impact of the physical environment (space allocation) on behaviors of the birds, with a 95% agreement in tracking the movement trajectories of the hens between the automated measurement and human labeling. This system enables researchers to more effectively assess the impact of housing and/or management factors or health status on bird behaviors.</p>
dc.description.comments <p>This article is from <em>Transactions of the ASABE</em> 57 (2014): 1455–1472, doi:<a href="" target="_blank">10.13031/trans.57.10505</a>. Posted with permission.</p>
dc.format.mimetype application/pdf
dc.identifier archive/
dc.identifier.articleid 1892
dc.identifier.contextkey 6266037
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath abe_eng_pubs/612
dc.language.iso en
dc.source.bitstream archive/|||Sat Jan 15 01:16:44 UTC 2022
dc.source.uri 10.13031/trans.57.10505
dc.subject.disciplines Agriculture
dc.subject.disciplines Bioresource and Agricultural Engineering
dc.subject.disciplines Poultry or Avian Science
dc.subject.keywords 3D vision
dc.subject.keywords Behavior monitoring
dc.subject.keywords Laying hen
dc.subject.keywords RFID
dc.subject.keywords Stocking density
dc.subject.keywords Tracking
dc.title Automated Tracking and Behavior Quantification of Laying Hens Using 3D Computer Vision and Radio Frequency Identification Technologies
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isAuthorOfPublication e60e10a5-8712-462a-be4b-f486a3461aea
relation.isAuthorOfPublication 36e0a8ce-fa2e-4df4-9f67-8d1717122650
relation.isOrgUnitOfPublication 8eb24241-0d92-4baf-ae75-08f716d30801
Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
2.08 MB
Adobe Portable Document Format