Morphology-Based In-Ovo Sexing of Chick Embryos Utilizing a Low-Cost Imaging Apparatus and Machine Learning

dc.contributor.authorZhang, Danielen
dc.contributor.authorJacobs, Leonieen
dc.date.accessioned2025-02-17T15:20:51Zen
dc.date.available2025-02-17T15:20:51Zen
dc.date.issued2025-01-29en
dc.date.updated2025-02-12T14:04:49Zen
dc.description.abstractThe routine culling of male chicks in the laying hen industry raises significant ethical, animal welfare, and sustainability concerns. Current methods to determine chick embryo sex before hatching are costly, time-consuming, and invasive. This study aimed to develop a low-cost, non-invasive solution to predict chick embryo sex before hatching using the morphological features of eggs. A custom imaging apparatus was created using a smartphone and light box, enabling consistent image capture of chicken eggs. Egg length, width, area, eccentricity, and extent were measured, and machine learning models were trained to predict chick embryo sex. The wide neural network model achieved the highest accuracy of 88.9% with a mean accuracy of 81.5%. Comparison of the imaging apparatus to a high-cost industrial 3D scanner demonstrated comparable accuracy in capturing egg morphology. The findings suggest that this method can contribute to the prevention of up to 6.2 billion male chicks from being culled annually by destroying male embryos before they develop the capacity to feel pain. This approach offers a feasible, ethical, and scalable alternative to current practices, with potential for further improvements in accuracy and adaptability to different industry settings.en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationZhang, D.; Jacobs, L. Morphology-Based In-Ovo Sexing of Chick Embryos Utilizing a Low-Cost Imaging Apparatus and Machine Learning. Animals 2025, 15, 384.en
dc.identifier.doihttps://doi.org/10.3390/ani15030384en
dc.identifier.urihttps://hdl.handle.net/10919/124592en
dc.language.isoenen
dc.publisherMDPIen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectmale chick cullingen
dc.subjectin-ovo sexingen
dc.subjectmachine learningen
dc.subjectmorphology-based analysisen
dc.subjectlaying hen industryen
dc.subjectanimal welfareen
dc.subjectlow-cost technologyen
dc.subjectnon-invasive sexingen
dc.titleMorphology-Based In-Ovo Sexing of Chick Embryos Utilizing a Low-Cost Imaging Apparatus and Machine Learningen
dc.title.serialAnimalsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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