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RESEARCH ARTICLE
Contents Vol 48(7)

Using video image analysis to count hens in cages and reduce egg breakage on collection belts

G. M. Cronin A C E , S. S. Borg A and M. T. Dunn B D
+ Author Affiliations
- Author Affiliations

A Animal Welfare Science Centre, Department of Primary Industries, Werribee, Vic. 3030, Australia.

B National Centre for Engineering in Agriculture, University of Southern Queensland, Toowoomba, Qld 4350, Australia.

C Present address: Faculty of Veterinary Science, University of Sydney, 425 Werombi Road, Camden, NSW 2570, Australia.

D CSIRO Exploration & Mining Group, 1 Technology Court, Pullenvale, Qld 4069, Australia.

E Corresponding author. Email: g.cronin@usyd.edu.au

Australian Journal of Experimental Agriculture 48(7) 768-772 https://doi.org/10.1071/EA07404
Submitted: 10 December 2007  Accepted: 23 March 2008   Published: 20 June 2008

Abstract

Stock people working in modern cage layer sheds spend more than half their daily work time directly checking hens to monitor health and welfare. In addition, mechanical egg collection belts must be checked for potential blockages that may result in cracked or broken eggs during the collection process. These are important tasks in the profitable management of modern multi-tier cage systems. However, where the upper tiers of cages are above stockperson eye level, the effectiveness of humans to perform these tasks accurately may be questioned. We investigated whether video image analysis (VIA, the ability of a computer to ‘see’) could automatically perform two common tasks – that of counting the number of hens per cage and scanning the egg collection belt to identify foreign (non-egg) objects. Cameras were attached to the robotic feeder that moved along the front of the cages. Views of the interior of the cages and the egg collection belt were recorded on digital video as the robotic feeder moved. Two VIA prototypes were evaluated, initially at the research institute and subsequently at a commercial farm. Using the respective automatic detection algorithms that were developed for the research, 79% of targets (hen legs) in cages were correctly counted, while 95% of foreign objects on the egg collection belt were detected. The results demonstrate that VIA can be used to monitor egg belts for potential blockages, and has potential as technology to count hens.

Additional keywords: cage housing, egg production, laying hens, machine vision.


Acknowledgements

We are grateful to DPI Victoria and the Australian Poultry CRC for co-funding the research, Bruce Schirmer for assistance with care of the hens at DPI Werribee, Philip Szepe for allowing us to conduct the on-farm component at Kinross Farm and Dr John Barnett for his comments on this manuscript.


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