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29 December 2000 Beef quality grading using machine vision
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Proceedings Volume 4203, Biological Quality and Precision Agriculture II; (2000) https://doi.org/10.1117/12.411743
Event: Environmental and Industrial Sensing, 2000, Boston, MA, United States
Abstract
A video image analysis system was developed to support automation of beef quality grading. Forty images of ribeye steaks were acquired. Fat and lean meat were differentiated using a fuzzy c-means clustering algorithm. Muscle longissimus dorsi (l.d.) was segmented from the ribeye using morphological operations. At the end of each iteration of erosion and dilation, a convex hull was fitted to the image and compactness was measured. The number of iterations was selected to yield the most compact l.d. Match between the l.d. muscle traced by an expert grader and that segmented by the program was 95.9%. Marbling and color features were extracted from the l.d. muscle and were used to build regression models to predict marbling and color scores. Quality grade was predicted using another regression model incorporating all features. Grades predicted by the model were statistically equivalent to the grades assigned by expert graders.
© (2000) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
S. Jeyamkondan, N. Ray, Glenn A. Kranzler, and Nisha Biju "Beef quality grading using machine vision", Proc. SPIE 4203, Biological Quality and Precision Agriculture II, (29 December 2000); https://doi.org/10.1117/12.411743
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