吉林大学学报(工学版) ›› 2014, Vol. 44 ›› Issue (6): 1811-1817.doi: 10.13229/j.cnki.jdxbgxb201406042

• • 上一篇    下一篇

基于I-RELIEF和SVM的畸形马铃薯在线分选

张保华1, 2, 黄文倩2, 李江波2, 赵春江1, 2, 刘成良1, 黄丹枫1   

  1. 1.上海交通大学 机械系统与振动国家重点实验室,上海 200240;
    2. 北京农业智能装备技术研究中心,北京 100097
  • 收稿日期:2013-04-26 出版日期:2014-11-01 发布日期:2014-11-01
  • 通讯作者: 赵春江(1964-),男,教授,博士生导师.研究方向:农业机械化与自动化,农业信息化,E-mail:zhaocj@nercita.org.cn
  • 作者简介:张保华(1986-),男,博士研究生.研究方向:基于机器视觉与模式识别的农产品无损检测.E-mail:
  • 基金资助:
    国家自然科学青年基金项目(31301236); “863”国家高技术研究发展计划项目(2013AA100307); 2012年北京市农林科学院博士后基金项目

Online sorting of irregular potatoes based on I-RELIEF and SVM method

ZHANG Bao-hua1, 2, HUANG Wen-qian2, LI Jiang-bo2, ZHAO Chun-jiang1, 2, LIU Cheng-liang1, HUANG Dan-feng1   

  1. 1.State Key Laboratory of Mechanical System and Vibration, Shanghai Jiaotong University, Shanghai 200240, China;
    2.Beijing Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China
  • Received:2013-04-26 Online:2014-11-01 Published:2014-11-01

RICH HTML

0   

PDF (PC)

448

摘要： 在提取的RGB图像的R分量图像上提取二值图像和边界图像,计算马铃薯图像的偏心度、矩形度、圆形度以及边界图像的10个傅立叶描述子等共计13个形状特征。其次把样本的形状特征输入到I-RELIEF模块中,求出各个形状特征在分类中的影响程度,即各自权值。然后把带权值的样本形状特征输入到支持向量机算法模块中进行训练,从而得到分类器。最后用分类器实现畸形马铃薯的在线分选。试验结果表明:该方法每秒钟可以检测4个马铃薯,可以满足分选设备的实时性要求,并且畸形马铃薯的识别率高达98.1%。

关键词: 机器视觉, 马铃薯分选, 傅立叶描述子, 在线检测, 支持向量机

Abstract: An online sorting method of irregular potatoes is proposed. First, the R-component image is extract, and then the binary image and boundary image are obtained by thresholding method. Second, thirteen essential geometrical features, such as eccentricity, rectangle degree and roundness, and ten Fourier descriptors are extracted. Third, the geometrical features of the potato image sample are feed into the module of I-RELIEF algorithm, which exports a weight for each feature. Fourth, the features of the potato image sample with weights are feed into the training module of Support Vector Machine (SVM). Finally, the classifier model is used to make decision and achieve the grading result online based on the potato's features and weights. Results show that the SVM method can detect and sort four potatoes per second with the help of I-RELIEF module, and the overall accuracy is 98.1%.

Key words: computer vision, potato sorting, fourier descriptor, in-line detection, support vector machine(SVM)

中图分类号: 

  • S126
[1] Moreda G P, Muñoz M A, Ruiz-Altisent M, et al. Shape determination of horticultural produce using two-dimensional computer vision—a review[J]. Journal of Food Engineering, 2012, 108(2):245-261.
[2] Zhang B H, Huang W Q, Li J B, et al. Principles, development and applications of computer vision for external quality inspection of fruits and vegetables: A review[J]. Food Research International, 2014, 62:326-343.
[3] Tao Y, Morrow C T, Heinemann P H, et al. Fourier-based separation technique for shape grading of potatoes using machine vision[J]. Transactions of the ASAE, 1995, 38(3):949-957.
[4] Corrado C, Francesca A, Federico P, et al. Shape analysis of agricultural products: a review of recent research advances and potential application to computer vision[J]. Food Bioprocess Technol, 2011, 4(5):673-692.
[5] Al-Mallahi A, Kataoka T, Okamoto H, et al. An image processing algorithm for detecting in-line potato tubers without singulation[J]. Computers and Electronics in Agriculture, 2010, 70(1):239-244.
[6] Gomes J F S, Leta F R. Application of computer vision techniques in the agriculture and food industry: a review[J]. European Food Research and Technology, 2012, 235(6):989-1000.
[7] Heinemann P H, Pathare N P, Morrow C T. An automated inspection station for machine-vision grading of potatoes[J]. Machine Vision and Applications, 1996, 9(1):14-19.
[8] Patel K K, Kar A, Jha S N, et al. Machine vision system: a tool for quality inspection of food and agricultural products[J]. Journal of Food Science and Technology, 2012, 49(2):123-141.
[9] Tao Y, Heinemann P H, Varghese Z. Machine vision for color inspection of potatoes and apples[J]. Transactions of the ASAE,1995,38(5):1555-1561.
[10] Zhou L, Chalana V, Kim Y. PC-based machine vision for real-time computer-aided potato inspection[J]. International Journal of Imaging Systems and Technology, 1998, 9(6):423-433.
[11] Navid R, Somayeh M B, Soleymani F. A real-time mathematical computer method for potato inspection using machine vision[J]. Computer and Mathematics with Application, 2012, 63(1):268-279.
[12] Gamal E, Sergio C, Enrique M,et al. In-line sorting of irregular potatoes by using automated computer-based machine vision system[J]. Journal of Food Engineering, 2012, 112(1-2):60-68.
[13] 郝敏,麻硕士,郝小冬. 基于Zernike矩的马铃薯形状检测[J]. 农业工程学报,2010,26(2):347-350. Hao Min, Ma Shuo-shi, Hao Xiao-dong. Potato shape detection based on Zernike moments[J]. Transactions of the Chinese Society of Agricultural Engineering, 2010, 26(2):347-350.
[14] 周竹,黄懿,李小昱,等. 基于机器视觉的马铃薯自动分级方法[J]. 农业工程学报,2012,28(7):178-183. Zhou Zhu, Huang Yi, Li Xiao-yu, et al. Automatic detecting and grading method of potatoes based on machine vision[J]. Transactions of the Chinese Society of Agricultural Engineering,2012,28(7):178-183.
[15] 郑冠楠,谭豫之,张俊雄,等. 基于计算机视觉的马铃薯自动检测分级[J]. 农业机械学报,2009,40(4):166-168,156. Zheng Guan-nan, Tan Yu-zhi, Zhang Jun-xiong,et al. Automatic detecting and grading method of potatoes with computer vision[J]. Transactions of the CSAM, 2009, 40(4):166-168, 156.
[16] Sun Yi-jun. Iterative RELIEF for feature weighting: algorithms, theories, and application[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2007,29(6):1035-1051.
[17] Cortes C,Vapuik V.Soft margin classifier:U.S.Patent 5640492[P].1997-6-17.
[1] 刘恩泽,吴文福. 基于机器视觉的农作物表面多特征决策融合病变判断算法[J]. 吉林大学学报(工学版), 2018, 48(6): 1873-1878.
[2] 隗海林, 包翠竹, 李洪雪, 李明达. 基于最小二乘支持向量机的怠速时间预测[J]. 吉林大学学报(工学版), 2018, 48(5): 1360-1365.
[3] 林金花, 王延杰, 王璐, 姚禹. 全局相机姿态优化下的快速表面重建[J]. 吉林大学学报(工学版), 2018, 48(3): 909-918.
[4] 耿庆田, 于繁华, 王宇婷, 高琦坤. 基于特征融合的车型检测新算法[J]. 吉林大学学报(工学版), 2018, 48(3): 929-935.
[5] 蔡振闹, 吕信恩, 陈慧灵. 基于反向细菌优化支持向量机的躯体化障碍预测模型[J]. 吉林大学学报(工学版), 2018, 48(3): 936-942.
[6] 周晓东, 张雅超, 谭庆昌, 张维君. 基于结构光视觉技术的圆柱度测量新方法[J]. 吉林大学学报(工学版), 2017, 47(2): 524-529.
[7] 张飞, 单忠德, 任永新, 聂军刚, 刘丰. 缸盖缺陷检测系统线阵相机现场标定方法[J]. 吉林大学学报(工学版), 2017, 47(2): 540-545.
[8] 袁哲明, 张弘杨, 陈渊. 基于特征选择和支持向量机的HIV-1型蛋白酶剪切位点预测[J]. 吉林大学学报(工学版), 2017, 47(2): 639-646.
[9] 梁士利, 魏莹, 潘迪, 张玲, 许廷发, 王双维. 基于语谱图行投影的特定人二字汉语词汇识别[J]. 吉林大学学报(工学版), 2017, 47(1): 294-300.
[10] 商强, 杨兆升, 张伟, 邴其春, 周熙阳. 基于奇异谱分析和CKF-LSSVM的短时交通流量预测[J]. 吉林大学学报(工学版), 2016, 46(6): 1792-1798.
[11] 赵云鹏, 于天来, 焦峪波, 宫亚峰, 宋刚. 异形桥梁损伤识别方法及参数影响分析[J]. 吉林大学学报(工学版), 2016, 46(6): 1858-1866.
[12] 周炳海, 徐佳惠. 基于支持向量机的多载量小车实时调度[J]. 吉林大学学报(工学版), 2016, 46(6): 2027-2033.
[13] 卢英, 王慧琴, 秦立科. 高大空间建筑火灾精确定位方法[J]. 吉林大学学报(工学版), 2016, 46(6): 2067-2073.
[14] 马知行, 赵琦, 张浩. 基于傅立叶分析的持家基因预测模型[J]. 吉林大学学报(工学版), 2016, 46(5): 1639-1643.
[15] 王品, 何璇, 吕洋, 李勇明, 邱明国, 刘书君. 基于多特征支持向量机和弹性区域生长的膝软骨自动分割[J]. 吉林大学学报(工学版), 2016, 46(5): 1688-1696.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
版权所有 © 《吉林大学学报(工学版)》编辑部
地址：长春市人民大街5988号　电话：+86-431-85095297　传真：+86-431-85094074　E-mail: xbgxb@jlu.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发