农业工程学报2016，Vol.32Issue(8)：272-277,6.DOI:10.11975/j.issn.1002-6819.2016.08.038
基于机器视觉图像特征参数的马铃薯质量和形状分级方法
Potato grading method of weight and shape based on imaging characteristics parameters in machine vision system
摘要
Abstract
Potato is cultivated as a major food resource in China. Manual grading is labor intensive. Machine vision system is one of the modern grading techniques and is becoming research focus. Weight and shape of potato are important indexes to divide potato grade. Generally, weight and shape of potato have significant positive correlation with outside dimension parameters of potatoes. It is the key to increase potato grading accuracy and speed in order to quickly obtain the imaging feature data possessing high correlation with potato weight and shape and to establish a strong correlation predictions estimation model for potato weight and shape. The focus of this research was to develop a potato grading method of weight and shape by means of image processing in the machine vision system. Firstly, the machine vision system was established, which can capture a potato’s three projection images simultaneously using a V-shaped plane mirror. One hundred potato samples were randomly selected, which were constituted of large, medium, small sizes, approximation sphere and approximation ellipsoidal according to artificial visual determination. Then the image feature parameters were obtained employing the digital image processing technology, including the contour areas in top view and two side views, the length and width of circumscribed rectangle in projection image of every potato sample. Secondly, the feature parameters with high weights value were selected using PCA (Principle Component Analysis) method in Unscramble software. The analysis results showed that the first two principal components explained 96% information contained in all characteristic data, and the scores of 100 potato samples were distributed in obvious three regions in the score graph with small size located the lower-left area, medium size located the middle area, and large size located the upper-right area. The predicted model of potato weight was constructed by means of multiple linear regression analysis using data of three contour areas in top view and two side views of every potato sample. The actual weight of the potato samples were gained by an electronic balance, and the correlation coefficient was 0.991. The distinguish accuracy were respective 90%, 100%, 90% for large, medium and small sizes in potato sample test set. Finally, potato shapes were analyzed by PCA using feature data, including the length and width of circumscribed rectangle in three projection image of potato samples. The score graph showed that the first two principal components explained 95% information contained in all feature data. The feature data scores were used to divide 100 potato samples into two types. In order to use the image characteristic parameters to determine the shape of potatoes, we set two dummy variables as -1 and 1, respectively, which represented approximation sphere and approximation ellipsoidal. The prediction model of potato shape was then established by the partial least squares discriminate analysis. The actual shape of the potato samples were decided by artificial ocular measurement, with a ratio of classification of 86.7%. Grading test of shape classification was completed for 40 potato samples in test set using the regression equation. A potato sample with a positive calculated value was judged as approximation ellipsoid, and a potato sample with a negative calculated value was judged as approximation spherical. Grading accuracies for approximation ellipsoid and approximation spherical were 83.3% and 89.3%, respectively. Our research indicated that the regression model for shape grading was reliable. Therefore the approach for non-detection inspecting potato weight and shape were effective and feasible, which can be applied in a potato grading system.关键词
无损检测/图像处理/分级/机器视觉/马铃薯/特征参数Key words
nondestructive examination/image processing/grading/machine vision/potato/characteristic parameters分类
信息技术与安全科学引用本文复制引用
王红军,熊俊涛,黎邹邹,邓建猛,邹湘军..基于机器视觉图像特征参数的马铃薯质量和形状分级方法[J].农业工程学报,2016,32(8):272-277,6.基金项目
国家自然科学基金项目 （）
