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

Article

Cronin, G. M., Borg, S. S. and Dunn, M. T.. 2008. "Using video image analysis to count hens in cages and reduce egg breakage on collection belts." Animal Production Science. 48 (7), pp. 768-772. https://doi.org/10.1071/EA07404
Article Title

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

ERA Journal ID5178
Article CategoryArticle
AuthorsCronin, G. M. (Author), Borg, S. S. (Author) and Dunn, M. T. (Author)
Journal TitleAnimal Production Science
Journal Citation48 (7), pp. 768-772
Number of Pages5
Year2008
PublisherCSIRO Publishing
Place of PublicationAustralia
ISSN0816-1089
1836-0939
1836-5787
Digital Object Identifier (DOI)https://doi.org/10.1071/EA07404
Web Address (URL)https://www.publish.csiro.au/an/EA07404
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. TwoVIA 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.

Keywordscage housing; egg production; laying hens; machine vision
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020400607. Signal processing
460304. Computer vision
300207. Agricultural systems analysis and modelling
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Institution of OriginUniversity of Southern Queensland
Byline AffiliationsDepartment of Primary Industries, Victoria
University of Sydney
University of Melbourne
National Centre for Engineering in Agriculture
Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia
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