| 基于机器视觉黄瓜果实自动分级方法 |
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| 引用本文： | 李明,房俊龙,乔翊博,刘超,张伶鳦,赵朝阳,栾吉玲.基于机器视觉黄瓜果实自动分级方法[J].农机化研究,2016(11):229-233. |
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| 作者姓名： | 李明 房俊龙 乔翊博 刘超 张伶鳦 赵朝阳 栾吉玲 |
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| 作者单位： | 东北农业大学电气与信息学院,哈尔滨,150030 |
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| 基金项目： | 黑龙江省博士后科研启动基金项目(LBH-Q13022);黑龙江省教育厅科学技术研究项目(12531004) |
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| 摘 要： | 为实现黄瓜果实快速准确分级,以摄像头为视频采集模块、DSP核心处理器为主控制模块、机械手为执行模块,并借助质量控制、电机传送等辅助单元,构建了自动化分级平台。参照国家标准NY/T1587-2008,利用图像处理方法对黄瓜果实图像的瓜长、把长、横径差、弓形高度进行了提取和计算。选取长春密刺、龙杂黄七号、露秋一号3个品种240根黄瓜果实作为试验样本,抽取每个品种的20个样本作为图像提取数据分析,其余60个样本作为自动分级平台测试。测试结果显示:该平台的平均分级精度为96.7%,每分钟约检测35根果实,相较人工分级具有快速、无损、准确、客观的特点,为机器视觉技术应用于椭长形果实自动化分级提供了重要依据。
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| 关 键 词： | 机器视觉 图像处理 黄瓜 果实 分级 机械手 |
Automatic Grading Method of Cucumber Fruits Based on Machine Vision |
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| Li Ming;Fang Junlong;Qiao Yibo;Liu Chao;Zhang Lingyi;Zhao Zhaoyang;Luan Jiling.Automatic Grading Method of Cucumber Fruits Based on Machine Vision[J].Journal of Agricultural Mechanization Research,2016(11):229-233. |
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| Authors: | Li Ming;Fang Junlong;Qiao Yibo;Liu Chao;Zhang Lingyi;Zhao Zhaoyang;Luan Jiling |
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| Institution: | Li Ming;Fang Junlong;Qiao Yibo;Liu Chao;Zhang Lingyi;Zhao Zhaoyang;Luan Jiling;Electrical and Information College,Northeast Agricultural University; |
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| Abstract: | To achieve the realization of grading cucumber fruit fast and accurately , we build an automation sub-base platform with the help of a camera as video acquisition module , DSP core processor as the main control module , as well as other auxiliary units like weight control and motor transport part .With reference to the national standard NY/T1587-2008 , we adopt the image processing method to extract and compute the melon length , pedicel length , diameter differ-ence , arcuate height of our cucumber fruit images , thus picking up 3 varieties 240 cucumber fruit as our test sample which contains 'Changchun Mici','Longzahuang VII'and ‘Luqiu I’.After, extract 20 samples of every variety as the image extracting data analysis and take the other 60 samples into the automation sub-base platform testing .Based on the test result , it shows: the average grade precision of the platform is 96 .7% while it can process 35 fruits per minute . Compared with the traditional manual classification , our platform has a characteristic of fast-processing , NDT, accurate and objective , which provides an important basis on applying machine vision technology into the automated classification of elliptical elongated fruit . |
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| Keywords: | machine vision image processing cucumber the fruit classification manipulator |
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