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- W2051892324 abstract "This paper presents the research results of the performance of classification methods for hyperspectral poultry imageryto identify fecal and ingesta contaminants on the surface of broiler carcasses. A pushbroom line-scan hyperspectralimager was used to acquire hyperspectral data with 512 narrow bands covered from 400 to 900 nm wavelengths. Threedifferent feces from digestive tracts (duodenum, ceca, colon), and ingesta were used as contaminants. Thesecontaminants were collected from the broiler carcasses fed by corn, milo, and wheat with soybean meals. For theselection of optimum classifier, various widely used supervised classification methods (parallelepiped, minimumdistance, Mahalanobis distance, maximum likelihood, spectral angle mapper, and binary coding) were investigated.The classification accuracies ranged from 62.94% to 92.27%. The highest classification accuracy for identifyingcontaminants for corn fed carcasses was 92.27% with spectral angle mapper classifier. While, the classificationaccuracy was 82.02% with maximum likelihood method for milo fed carcasses and 91.16% accuracy was obtained forwheat fed carcasses when same classification method was used. The mean classification accuracy obtained in thisstudy for classifying fecal and ingesta contaminants was 90.21%." @default.
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- W2051892324 date "2004-03-30" @default.
- W2051892324 modified "2023-09-23" @default.
- W2051892324 title "Classification of hyperspectral imagery for identifying fecal and ingesta contaminants" @default.
- W2051892324 doi "https://doi.org/10.1117/12.514724" @default.
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