Multi-sensor data fusion for detection of woody breast myopathy in the poultry industry (Conference Presentation)

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

doi:10.1117/12.2519084

14 May 2019 Multi-sensor data fusion for detection of woody breast myopathy in the poultry industry (Conference Presentation)

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

Author Affiliations +

Proceedings Volume 11016, Sensing for Agriculture and Food Quality and Safety XI; 1101605 (2019) https://doi.org/10.1117/12.2519084
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

Abstract

This study is concerned with development of multi-sensor data fusion technology to detect woody breast fillets in the poultry industry. The common practice in commercial plants for detection of woody breast fillets is through subjective evaluations of various visual traits although the woody breast myopathy is uniquely distinguished with tactile attributes of muscle hardness and rigidity of fillets. This study extends and improves the previously developed rapid and non-invasive 2D machine vision technique that measures muscle rigidity using a single 2D camera to detect woody breast fillets moving on conveyor system. This 2D machine vision technology is currently under development for commercialization. In this study, multi-sensor data fusion of 2D and 3D shapes and color features is proposed to further improve the performance of the single camera-based technology. A preliminary study found that information fusion of different physical properties such as muscle rigidity, muscle out-bulging shape, and presence of hemorrhagic lesions on the skin-side surface of the fillets could improve the detection accuracy than that provided by individual sensors.

Conference Presentation

Citation Download Citation

Seung-Chul Yoon, Brian Bowker, Kurt C. Lawrence, and Hong Zhuang "Multi-sensor data fusion for detection of woody breast myopathy in the poultry industry (Conference Presentation)", Proc. SPIE 11016, Sensing for Agriculture and Food Quality and Safety XI, 1101605 (14 May 2019); https://doi.org/10.1117/12.2519084

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