基于机器视觉的马铃薯自动分级方法

为了实现马铃薯的自动分级,设计了基于V型平面镜同时获取三面图像的马铃薯机器视觉分级系统,并提出了相应的分级算法。根据大小特性,提出基于最小外接柱体体积法的马铃薯大小分级检测方法;根据外形特性,采用最长径外接矩形的宽高比法,实现了类圆形、椭圆型以及长型马铃薯的分类;根据马铃薯缺陷特点,分别提出以缺陷面积大小作为判别准则的孔洞、干腐马铃薯判别方法,以外接矩形对角线长度作为判别准则的机械损伤马铃薯判别方法和基于交叉法的发芽、畸形马铃薯检测方法。最终马铃薯的分级正确率为91.0%。试验结果表明:基于机器视觉的马铃薯自动分级检测方法可行,可用于马铃薯外部品质的在线检测。

Automatic detecting and grading method of potatoes based on machine vision

In order to realize grading of potato, a potato grading system based on machine vision was developed. Two pieces of plane mirror placed in V-shape were used to get three surface images of a potato at one time. Volume method based on minimum circumscribed cylinder was proposed to grade potatoes according to their size, the ratio of width and length of the longest diameter circum-rectangle was used to grade potatoes according to their shape. On the basis of characteristics of potato defects, defect area, diagonal length and cross method were used as criterions of the lacunary, dry rot, mechanical damaged, budded and misshapen potatoes. Experiments showed that the recognition accuracy of potato defects was 91.0%. The results indicate that the classification method has a high accuracy, and can be used for external quality online detection of potatoes.