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Proceedings Paper

Poultry carcass inspection by a fast line-scan imaging system: results from in-plant testing
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Paper Abstract

During in-plant testing of a hyperspectral line-scan imaging system, images were acquired of wholesome and systemically diseased chickens on a commercial processing line moving at a speed 70 birds per minute. A fuzzy logic based algorithm using four key wavelengths, 468 nm, 501 nm, 582 nm, 629 nm, was developed using image data from the validation set of images of 543 wholesome and 66 systemically diseased chickens. A classification method using the fuzzy logic based algorithm was then tested on the testing set of images of 457 wholesome and 37 systemically diseased chickens, as well as 80 systemically diseased chickens that were imaged off-shift during breaks between normal processing shifts of the chicken plant. The classification method correctly identified 89.7% of wholesome chicken images and 98.5% of systemically diseased chicken images in the validation set. For the testing data set, the method correctly classified 96.7 % of 457 wholesome chicken images and 100% of 37 systemically diseased chicken images. The 80 images acquired off-shift were also 100% correctly identified.

Paper Details

Date Published: 23 October 2006
PDF: 11 pages
Proc. SPIE 6381, Optics for Natural Resources, Agriculture, and Foods, 63810V (23 October 2006); doi: 10.1117/12.686144
Show Author Affiliations
Kuanglin Chao, USDA Agricultural Research Service (United States)
Chun-Chieh Yang, USDA Agricultural Research Service (United States)
Yud-Ren Chen, USDA Agricultural Research Service (United States)
Diane E. Chan, USDA Agricultural Research Service (United States)
Moon S. Kim, USDA Agricultural Research Service (United States)


Published in SPIE Proceedings Vol. 6381:
Optics for Natural Resources, Agriculture, and Foods
Yud-Ren Chen; George E. Meyer; Shu-I Tu, Editor(s)

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