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     Advance Journal of Food Science and Technology


Evaluation of Beef Marbling Grade Based on Advanced Watershed Algorithm and Neural Network

1Xiangyan Meng, 2Yonghai Sun, 1Yuan Ni and 1Yumiao Ren
1College of Electronic Information Engineering, Xi'an Technological University, 710032, China
2College of Biological and Agricultural Engineering, Jilin University, 130022, China
Advance Journal of Food Science and Technology  2014  2:206-211
http://dx.doi.org/10.19026/ajfst.6.11  |  © The Author(s) 2014
Received: September 18, 2013  |  Accepted: November 05, 2013  |  Published: February 10, 2014

Abstract

As to the problem of inaccurate in traditional grade method of beef marbling, a automatic grading system based on computer vision had been founded and was used to predict the beef quality grade of Chinese yellow cattle. Image processing was used to automatically evaluate the beef marbling grade. Segmentation methods used in rib-eye image of beef carcass was improved watershed algorithm. All grading indicators were obtained by image processing automatically. Four grading indicators, which characterize the size, number and distribution of marbling particles, were proposed for the inputs of neural network prediction model. The experimental results indicated that the image processing methods were effective. The grading system based on computer vision and neural network model can better predict the beef quality grading. The prediction accuracy of beef marbling grade was 86.84%. Algorithm proposed in this study proved the image processing and neural network modeling is an effective method for beef marbling grading.

Keywords:

Beef marbling grade, improved watershed, machine vision, neural network,


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Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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The authors have no competing interests.

ISSN (Online):  2042-4876
ISSN (Print):   2042-4868
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