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Proceedings Paper

Systemically diseased chicken identification using multispectral images and region of interest analysis
Author(s): Chun-Chieh Yang; Kuanglin Chao; Yud-Ren Chen; Howard L. Early
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Paper Abstract

A simple multispectral classification method for the identification of systemically diseased chickens was developed and tested between two different imaging systems. An image processing algorithm was developed to define and locate the region of interest (ROI) as classification areas on the image. The average intensity was calculated for each classification area of the chicken image. A decision tree algorithm was used to determine threshold values for each classification areas. The wavelength of 540 nm was used for image differentiation purpose. There were 164 wholesome and 176 systemically diseased chicken images collected using the first imaging system, and 332 wholesome and 318 systemically diseased chicken images taken by the second imaging system. The differentiation thresholds, generated by the decision tree method, based on the images from the first imaging system were applied to the images from the second imaging system, and vice versa. The accuracy from evaluation was 95.7% for wholesome and 97.7% of systemically diseased chickens for the first image batch, and 99.7% for wholesome and 93.5% for systemically diseased chickens for the second image batch. The result showed that using single wavelength and threshold, this simple classification method can be used in automated on-line applications for chicken inspection.

Paper Details

Date Published: 19 November 2004
PDF: 12 pages
Proc. SPIE 5587, Nondestructive Sensing for Food Safety, Quality, and Natural Resources, (19 November 2004); doi: 10.1117/12.571488
Show Author Affiliations
Chun-Chieh Yang, U.S. Dept. of Agriculture (United States)
Kuanglin Chao, U.S. Dept. of Agriculture (United States)
Yud-Ren Chen, U.S. Dept. of Agriculture (United States)
Howard L. Early, U.S. Dept. of Agriculture (United States)

Published in SPIE Proceedings Vol. 5587:
Nondestructive Sensing for Food Safety, Quality, and Natural Resources
Yud-Ren Chen; Shu-I Tu, Editor(s)

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