Evaluating pork grade by digital image processing

Yu Bo; Zheng Limin; Ren Fazheng; Tian Lijun

Volume 23 Issue 4

Apr. 2007

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Abstract

Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE) > 2007 > 23(4): 242-248.

Yu Bo, Zheng Limin, Ren Fazheng, Tian Lijun. Evaluating pork grade by digital image processing[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2007, 23(4): 242-248.

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Evaluating pork grade by digital image processing

1.
College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
2.
College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China

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Received Date: November 27, 2005
Revised Date: October 25, 2006
Published Date: April 29, 2007

Graphical Abstract

Abstract

Abstract

Left half carcass and loin eye pictures of 80 pigs were taken with a digital camera with fixed lens length and focus. After image processing, features were abstracted from the images. The correlative image features and the grades were used to train a Back Propagation Neural Network(BPNN) based on Digital Image Processing(DIP). Results indicate that fat thickness has significant relationship with image fat thickness(p<0.01). Carcass yield is correlative with image hunkers(p<0.01). Loin-eye area has a strong relationship with image loin-eye area(p<0.01). Muscle color is correlative with the mean 2G-B and the mean R+G of lean pixels in loin-eye region(p<0.01). Intramuscular fat characteristic is correlative with image intramuscular fat characteristic(p<0.01). Lean meat percentage was correlative with image fat thickness and image loin-eye area(p<0.01). In conclusion, the BPNN based on DIP can be used to evaluate pork grading quickly and accurately.
- digital image processing,
- pork grading,
- back propagation neural network

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