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Classification of Unhealthy Chicken based on Chromaticity of the Comb

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dc.contributor.authorBakar M.A.A.A.en_US
dc.contributor.authorKer P.J.en_US
dc.contributor.authorTang S.G.H.en_US
dc.contributor.authorLee H.J.en_US
dc.contributor.authorZainal B.S.en_US
dc.contributor.authorid58109032700en_US
dc.contributor.authorid37461740800en_US
dc.contributor.authorid57853430300en_US
dc.contributor.authorid57190622221en_US
dc.contributor.authorid58109944100en_US
dc.date.accessioned2023-05-29T09:38:46Z
dc.date.available2023-05-29T09:38:46Z
dc.date.issued2022
dc.descriptionAgriculture; Animals; Bacteria; Color; Feature extraction; Learning algorithms; Learning systems; Losses; Supervised learning; Chicken; Chromaticity; Disease outbreaks; Economic loss; Human health; Human observations; Laboratory test; Machine-learning; Performance; Supervised machine learning; Virusesen_US
dc.description.abstractHuman observation and laboratory tests are the traditional method for identifying bacteria- or virus-infected chicken, but these methods may result in late detection. Major disease outbreaks may occur, leading to significant economic loss and threatening human health. Therefore, this paper reports on the utilization of a supervised machine learning algorithm to provide early detection of bacteria- or virus-infected chickens based on their comb's colour feature. Current work utilizes a well-established, International Commission on Illumination (CIE) XYZ colour space to investigate the change in the colour of the infected and healthy chicken comb. A logistic regression model was developed and proposed to classify the chickens, and the performance of the model was revealed. The chromaticity analysis shows that the comb chromaticity of the infected chicken was changing from the red to green part, based on the x chromaticity value. The performance of the proposed model indicates that this algorithm can classify between infected and healthy chickens with 100% sensitivity and 83% specificity. This work has demonstrated a new feature that can serve as an indicator for detecting bacteriaor virus-infected chickens, and contributes to the development of modern technology in agriculture applications. � 2022 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/ICOCO56118.2022.10031812
dc.identifier.epage5
dc.identifier.scopus2-s2.0-85148415374
dc.identifier.spage1
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85148415374&doi=10.1109%2fICOCO56118.2022.10031812&partnerID=40&md5=520cd857e9223edc17125f752d96c7d9
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/27022
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitle2022 IEEE International Conference on Computing, ICOCO 2022
dc.titleClassification of Unhealthy Chicken based on Chromaticity of the Comben_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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