Real-time machine vision system for online detection of wooden breast myopathy in chicken fillets (Conference Presentation)

Seung-Chul Yoon; Brian Bowker; Kurt Lawrence; Hong Zhuang

doi:10.1117/12.2305149

15 May 2018 Real-time machine vision system for online detection of wooden breast myopathy in chicken fillets (Conference Presentation)

Seung-Chul Yoon, Brian Bowker, Kurt Lawrence, Hong Zhuang

Author Affiliations +

Proceedings Volume 10665, Sensing for Agriculture and Food Quality and Safety X; 106650H (2018) https://doi.org/10.1117/12.2305149
Event: SPIE Commercial + Scientific Sensing and Imaging, 2018, Orlando, FL, United States

Abstract

The advancement of the broiler industry in meat processing efficiency and production yield is remarkable. However, the industry has also experienced an emerging meat quality defect, called wooden breast syndrome. The symptoms of wooden breast syndrome include hardened muscle, pale color, ridge-like bulging, connective tissue accumulation, and/or rubbery texture. This study is concerned with the latest research progress within USDA-ARS to develop real-time machine vision system for rapid online detection of wooden breast fillets in the broiler industry. Because the current industry method of wooden breast detection is through tactile evaluation and product handling by humans, a rapid and non-invasive sensing technique to detect meat products affected by wooden breast syndrome is invaluable to both the industry and the scientific community. The developed machine vision system was designed to detect breast fillets moving on a conveyor belt system and differentiate between normal and wooden breast fillets. The imaging system captures and analyzes the physical properties that are correlated with severity of wooden breast condition. The machine vision system consists of a digital CMOS camera, a lighting system, a computer, and software. Shape descriptors characterizing differences between contours of normal and affected breast fillets were developed. Preliminary results obtained with 45 fillets (15 normal, 15 moderate wooden breast, and 15 severe wooden breast) indicated 98 % overall accuracy with a 6.7% false positive rate for normal fillets. A discussion for its commercialization is ongoing with an industry partner.

Conference Presentation

Citation Download Citation

Seung-Chul Yoon, Brian Bowker, Kurt Lawrence, and Hong Zhuang "Real-time machine vision system for online detection of wooden breast myopathy in chicken fillets (Conference Presentation)", Proc. SPIE 10665, Sensing for Agriculture and Food Quality and Safety X, 106650H (15 May 2018); https://doi.org/10.1117/12.2305149

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KEYWORDS

Breast

Machine vision

Computing systems

Imaging systems

CMOS cameras

Connective tissue

Light sources and illumination

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