农业工程学报2007，Vol.23Issue(4)：242-248,7.
利用图像处理技术评定猪肉等级
Evaluating pork grade by digital image processing
摘要
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). Loineye 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.关键词
图像处理技术/猪肉等级/人工神经网络Key words
digital image processing/pork grading/back propagation neural network分类
农业科技引用本文复制引用
于铂,郑丽敏,任发政,田立军..利用图像处理技术评定猪肉等级[J].农业工程学报,2007,23(4):242-248,7.基金项目
Chinese National 863 Projects Council(2002AA248051-2) （2002AA248051-2）
