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http://dx.doi.org/10.5187/jast.2022.e56

Current advances in detection of abnormal egg: a review  

Jun-Hwi, So (Department of Smart Agriculture Systems, Chungnam National University)
Sung Yong, Joe (Department of Biosystems Machinery Engineering, Chungnam National University)
Seon Ho, Hwang (Department of Smart Agriculture Systems, Chungnam National University)
Soon Jung, Hong (Department of Liberal Arts, Korea National University of Agriculture and Fisheries)
Seung Hyun, Lee (Department of Smart Agriculture Systems, Chungnam National University)
Publication Information
Journal of Animal Science and Technology / v.64, no.5, 2022 , pp. 813-829 More about this Journal
Abstract
Internal and external defects of eggs should be detected to prevent cross-contamination of intact eggs by abnormal eggs during storage. Emerging detection technologies for abnormal eggs were introduced as an alternative to human inspection. The advanced technologies could rapidly detect abnormal eggs. Abnormal egg detection technologies using acoustic response, machine vision, and spectroscopy have been commercialized in the poultry industry. Non-destructive egg quality assessment methods meanwhile could preserve the value of eggs and improve detection efficiency. In order to improve detection efficiency, it is essential to select a proper algorithm for classifying the types of abnormal eggs. This review deals with the performance of the detection technologies for various types of abnormal eggs in recently published resources. In addition, the discriminant methods and detection algorithms of abnormal eggs reported in the published literature were investigated. Although the majority of the studies were conducted on a laboratory scale, the developed detection technologies for internal and external defects in eggs were technically feasible to obtain the excellent detection accuracy. To apply the developed detection technologies to the poultry industry, it is necessary to achieve the detection rates required from the industry.
Keywords
Abnormal egg detection; Machine vision; Spectroscopy; Acoustic response; Modified pressure; Hyperspectral imaging;
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