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Proceedings Paper

Classification of hyperspectral imagery for identifying fecal and ingesta contaminants
Author(s): Bosoon Park; William R. Windham; Kurt C. Lawrence; Douglas P. Smith
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Paper Abstract

This paper presents the research results of the performance of classification methods for hyperspectral poultry imagery to identify fecal and ingesta contaminants on the surface of broiler carcasses. A pushbroom line-scan hyperspectral imager was used to acquire hyperspectral data with 512 narrow bands covered from 400 to 900 nm wavelengths. Three different feces from digestive tracts (duodenum, ceca, colon), and ingesta were used as contaminants. These contaminants were collected from the broiler carcasses fed by corn, milo, and wheat with soybean meals. For the selection of optimum classifier, various widely used supervised classification methods (parallelepiped, minimum distance, 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 identifying contaminants for corn fed carcasses was 92.27% with spectral angle mapper classifier. While, the classification accuracy was 82.02% with maximum likelihood method for milo fed carcasses and 91.16% accuracy was obtained for wheat fed carcasses when same classification method was used. The mean classification accuracy obtained in this study for classifying fecal and ingesta contaminants was 90.21%.

Paper Details

Date Published: 30 March 2004
PDF: 10 pages
Proc. SPIE 5271, Monitoring Food Safety, Agriculture, and Plant Health, (30 March 2004); doi: 10.1117/12.514724
Show Author Affiliations
Bosoon Park, U.S. Dept. of Agriculture (United States)
William R. Windham, U.S. Dept. of Agriculture (United States)
Kurt C. Lawrence, U.S. Dept. of Agriculture (United States)
Douglas P. Smith, U.S. Dept. of Agriculture (United States)

Published in SPIE Proceedings Vol. 5271:
Monitoring Food Safety, Agriculture, and Plant Health
George E. Meyer; Yud-Ren Chen; Shu-I Tu; Bent S. Bennedsen; Andre G. Senecal, Editor(s)

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