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Machine Vision for Detecting Internal Fecal Contaminants of Broiler Carcasses

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  Paper number  033051,  2003 ASAE Annual Meeting . (doi: 10.13031/2013.15000) @2003
Authors:   Bosoon Park, Kurt C. Lawrence, William R. Windham, Douglas P. Smith, Peggy Feldner
Keywords:   Machine Vision, Image Processing, Food Safety, Poultry Inspection, Hyperspectral, Multispectral Imaging, Fecal Contamination, Ceca, Internal Contaminants

Detecting fecal contaminants in the visceral cavity of the broiler is difficult but extremely important for poultry safety inspection. A hyperspectral imaging system could be used effectively for detecting internal cecal contaminants on halves broiler carcasses. Two 565 and 517-nm wavelength images were selected from 512 calibrated hypercube image data. Image processing algorithms, band ratio, threshold, and median filtering, were useful to identify fecal contaminants from the internal cavity. The accuracy of detection algorithms to identify cecal contaminants was varied with fecal threshold values and median filter as well. The imaging system easily identified cecal contaminants with 92.5% detection accuracy but also incorrectly identified 123 carcass features that were not considered as contaminants (false positives) and missed 15 actual contaminants (7.6% Type I error) when fecal threshold value of 1.05 was employed. The higher accuracy (96.9%) and lower missed contaminants (3.0% Type I error) could be obtained when different fecal threshold value was used. However, in this case, false positives drastically increased.

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