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Detection of Stress Cracks in Rice Kernels Based on Machine Vision

机译：基于机器视觉的稻仁应力裂纹检测

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摘要

A machine vision system was developed to detect different types of stress cracks in rice kernels. An image processing algorithm was used to enhance the object and reduce noise in the acquired image. Rice kernels were classified as those with zero, single, double or multiple stress cracks. Zero and single stress cracks were the easiest to detect. Careful positioning of the kernel over the lighting aperture was necessary for accurate detection of double and multiple stress cracks. This system provided an average accuracy of approximately 96.5 % for no cracks, 93.4 % for a single crack, 84.2 % for double cracks and 83.4 % for multiple cracks compared to human inspection. The processing time was between 0.45 and 0.12 s/ kernel.
机译：开发了一种机器视觉系统来检测稻仁中不同类型的应力裂纹。使用图像处理算法来增强对象并减少所获取图像中的噪声。水稻籽粒被分类为具有零，单，双或多应力裂纹的那些。零应力裂纹和单应力裂纹最容易检测到。为准确检测双重和多重应力裂纹，必须将内核仔细定位在照明孔上。与人工检查相比，该系统提供的平均精度为：无裂纹，单裂纹93.4％，双裂纹84.2％和多裂纹83.4％，无裂纹。处理时间介于0.45和0.12 s /内核之间。

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    Xu Lizhang; Li Yaoming;

  • 作者单位

    Institute of Agricultural Engineering Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education & Jiangsu Province, Jiangsu University Zhenjiang-212 013, Jiangsu Province China;

    Institute of Agricultural Engineering Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education & Jiangsu Province, Jiangsu University Zhenjiang-212 013, Jiangsu Province China;

  • 收录信息 美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
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