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.
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.
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