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Beef Tenderness
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<blockquote data-quote="MikeC" data-source="post: 200716" data-attributes="member: 1604"><p>Coming soon to a packer near you:</p><p></p><p>Predicting Beef Tenderness with Computer Vision</p><p></p><p>Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan <a href="http://www.asabe.org" target="_blank">http://www.asabe.org</a></p><p>Citation: Paper number 013063, </p><p>Authors: S. Jeyamkondan, G. A. Kranzler, A. Lakshmikanth</p><p>Keywords: Computer vision, beef tenderness, Warner-Bratzler shear force, textural features, image processing</p><p></p><p>A computer vision system was developed to predict cooked-beef tenderness. Our objective was to predict Warner-Bratzler shear force on 14-day aged beef using textural features extracted from 110 fresh beef color images. After ribeye segmentation, images were converted from RGB to CIELAB color space. Gray-level difference histograms were constructed from each L*, a*, and b* color bands and textural features were extracted. The system predicted shear force with an R 2 value of 0.50 and correctly classified 79% of samples into two tenderness categories.</p><p></p><p>Detailed close-up images were also acquired from the final 48 samples. Textural features extracted from these close-up images captured more textural information and predicted shear force with a higher R 2 value of 0.72. Correct classification rate was 92%.</p></blockquote><p></p>
[QUOTE="MikeC, post: 200716, member: 1604"] Coming soon to a packer near you: Predicting Beef Tenderness with Computer Vision Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan [url=http://www.asabe.org]http://www.asabe.org[/url] Citation: Paper number 013063, Authors: S. Jeyamkondan, G. A. Kranzler, A. Lakshmikanth Keywords: Computer vision, beef tenderness, Warner-Bratzler shear force, textural features, image processing A computer vision system was developed to predict cooked-beef tenderness. Our objective was to predict Warner-Bratzler shear force on 14-day aged beef using textural features extracted from 110 fresh beef color images. After ribeye segmentation, images were converted from RGB to CIELAB color space. Gray-level difference histograms were constructed from each L*, a*, and b* color bands and textural features were extracted. The system predicted shear force with an R 2 value of 0.50 and correctly classified 79% of samples into two tenderness categories. Detailed close-up images were also acquired from the final 48 samples. Textural features extracted from these close-up images captured more textural information and predicted shear force with a higher R 2 value of 0.72. Correct classification rate was 92%. [/QUOTE]
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