Automated Tracking and Behavior Quantification of Laying Hens Using 3D Computer Vision and Radio Frequency Identification Technologies
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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.
This article is from Transactions of the ASABE 57 (2014): 1455–1472, doi:10.13031/trans.57.10505. Posted with permission.