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Translating conventional wisdom on chicken comb color into automated monitoring of disease-infected chicken using chromaticity-based machine learning models

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dc.citedby3
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.authorBaharuddin M.Z.en_US
dc.contributor.authorLee H.J.en_US
dc.contributor.authorOmar A.R.en_US
dc.contributor.authorid58109032700en_US
dc.contributor.authorid37461740800en_US
dc.contributor.authorid57853430300en_US
dc.contributor.authorid35329255600en_US
dc.contributor.authorid57190622221en_US
dc.contributor.authorid7202864053en_US
dc.date.accessioned2024-10-14T03:20:50Z
dc.date.available2024-10-14T03:20:50Z
dc.date.issued2023
dc.description.abstractBacteria- or virus-infected chicken is conventionally detected by manual observation and confirmed by a laboratory test, which may lead to late detection, significant economic loss, and threaten human health. This paper reports on the development of an innovative technique to detect bacteria- or virus-infected chickens based on the optical chromaticity of the chicken comb. The chromaticity of the infected and healthy chicken comb was extracted and analyzed with International Commission on Illumination (CIE) XYZ color space. Logistic Regression, Support Vector Machines (SVMs), K-Nearest Neighbors (KNN), and Decision Trees have been developed to detect infected chickens using the chromaticity data. Based on the X and Z chromaticity data from the chromaticity analysis, the color of the infected chicken�s comb converged from red to green and yellow to blue. The development of the algorithms shows that Logistic Regression, SVM with Linear and Polynomial kernels performed the best with 95% accuracy, followed by SVM-RBF kernel, and KNN with 93% accuracy, Decision Tree with 90% accuracy, and lastly, SVM-Sigmoidal kernel with 83% accuracy. The iteration of the probability threshold parameter for Logistic Regression models has shown that the model can detect all infected chickens with 100% sensitivity and 95% accuracy at the probability threshold of 0.54. These works have shown that, despite using only the optical chromaticity of the chicken comb as the input data, the developed models (95% accuracy) have performed exceptionally well, compared to other reported results (99.469% accuracy) which utilize more sophisticated input data such as morphological and mobility features. This work has demonstrated a new feature for bacteria- or virus-infected chicken detection and contributes to the development of modern technology in agriculture applications. Copyright � 2023 Bakar, Ker, Tang, Baharuddin, Lee and Omar.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo1174700
dc.identifier.doi10.3389/fvets.2023.1174700
dc.identifier.scopus2-s2.0-85164531446
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85164531446&doi=10.3389%2ffvets.2023.1174700&partnerID=40&md5=41642c33de3f7c20688a78cca811e5bf
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34582
dc.identifier.volume10
dc.publisherFrontiers Media SAen_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofGold Open Access
dc.relation.ispartofGreen Open Access
dc.sourceScopus
dc.sourcetitleFrontiers in Veterinary Science
dc.subjectagriculture
dc.subjectchicken comb
dc.subjectchromaticity
dc.subjectclassification model
dc.subjectdiseases-infected chicken
dc.subjectenergy
dc.subjectimage processing
dc.subjectmachine learning
dc.subjectaccuracy
dc.subjectArticle
dc.subjectbacterium detection
dc.subjectclassifier
dc.subjectcolorimetry
dc.subjectcomparative study
dc.subjectdata analysis
dc.subjectdata extraction
dc.subjectdecision tree
dc.subjectGallus gallus
dc.subjecthealth status
dc.subjectillumination
dc.subjectimage processing
dc.subjectk nearest neighbor
dc.subjectkernel method
dc.subjectlearning algorithm
dc.subjectmachine learning
dc.subjectmorphological trait
dc.subjectnonhuman
dc.subjectperformance
dc.subjectprobability
dc.subjectsensitivity and specificity
dc.subjectsupervised machine learning
dc.subjectsupport vector machine
dc.subjectvirus infection
dc.subjectwalking speed
dc.titleTranslating conventional wisdom on chicken comb color into automated monitoring of disease-infected chicken using chromaticity-based machine learning modelsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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