您好，欢迎访问北京市农林科学院 机构知识库！
北京市农林科学院机构知识库

全部

  • 全部
  • 标题
  • 学者
  • 机构
  • 关键词

Multispectral detection of skin defects of bi-colored peaches based on vis-NIR hyperspectral imaging

文献类型： 外文期刊

作者： Li, Jiangbo 1 ; Chen, Liping 1 ; Huang, Wenqian 1 ; Wang, Qingyan 1 ; Zhang, Baohua 1 ; Tian, Xi 1 ; Fan, Shuxiang 2 ; Li 1 ;

作者机构： 1.Beijing Res Ctr Intelligent Equipment Agr, Beijing 100097, Peoples R China

2.Natl Res Ctr Intelligent Equipment Agr, Beijing 100097, Peoples R China

3.Minist Agr, Key Lab Agriinformat, Beijing 100097, Peoples R China

4.Beijing Key Lab Intelligent Equipment Technol Agr, Beijing 100097, Peoples R China

关键词： Bi-colored fruit;Peach;Defect detection;Hyperspectral imaging;Multispectral images;Band ratio

期刊名称：POSTHARVEST BIOLOGY AND TECHNOLOGY （ 影响因子：5.537； 五年影响因子：5.821 ）

ISSN：

年卷期：

页码：

收录情况： SCI

摘要： Fruit skin defects may cause fruit spoilage, reduce commodity economic value, and give rise to food quality and safety concerns. Therefore, one of the main tasks of post-harvest processing of fruit is the detection of skin defects by machine vision technology. However, inspection of skin defects on bi-colored fruit varieties by image processing is more difficult because of the high variability of the skin color. This article presents a multispectral detection method for skin defects of bi-colored 'Pinggu' peaches based on visible-near infrared (vis-NIR) hyperspectral imaging. Peaches with nine types of skin condition including skin injury, scarring, insect damage, puncture injury, decay, disease spots, dehiscent scarring and anthracnose and normal surface were studied. Principal component analysis (PCA) was used to reducehyperspectral data dimensionality to select several wavelengths that could potentiallybe used in an in-line multispectral imaging system. Different defect types produced an obvious feature only in some specific PC images depending on whether the visible light spectrum (425-780 nm), the near infrared spectrum (781-1000 nm), the full-spectrum (400-1000 nm) or only characteristic wavelengths (463, 555, 687, 712, 813, 970 nm or 781, 815, 848 nm) were used. A two-band ratio image (Q(781/848)) was successfully used to differentiate defects from a normal surface. Finally, a detection algorithm for skin defects was developed based on a band ratio (Q(781/848)) coupled with a simple thresholding method. For the investigated 145 independent test samples with nine skin conditions, an accuracy of 96.6% was obtained, indicating that the proposed multispectral algorithm was effective in differentiating normal and defective bi-colored peaches. The proposed algorithm can be extended to other fruit. (C) 2015 Elsevier B.V. All rights reserved.

  • 相关文献

[1]Comparative analysis of models for robust and accurate evaluation of soluble solids content in 'Pinggu' peaches by hyperspectral imaging. Chen, Liping. 2017

[2]Detection of defects on apple using B-spline lighting correction method. Li, Jiangbo,Huang, Wenqian,Guo, Zhiming. 2013

[3]Automatic detection of defective apples using NIR coded structured light and fast lightness correction. Zhang, Chi,Zhao, Chunjiang,Huang, Wenqian,Wang, Qingyan,Liu, Shenggen,Li, Jiangbo,Guo, Zhiming.

[4]HYPERSPECTRAL IMAGE FOR DISCRIMINATING APHID AND APHID DAMAGE REGION OF WINTER WHEAT LEAF. Luo Juhua,Huang Wenjiang,Guan Qingsong,Zhao Jinling,Zhang Jingcheng. 2013

[5]Identification of seedling cabbages and weeds using hyperspectral imaging. Wei, Deng,Zhao Chunjiang,Xiu, Wang,Huang, Yanbo,Wei, Deng,Zhao Chunjiang,Xiu, Wang,Wei, Deng,Zhao Chunjiang,Xiu, Wang,Wei, Deng,Zhao Chunjiang,Xiu, Wang. 2015

[6]Effectively Predicting Soluble Solids Content in Apple Based on Hyperspectral Imaging. Huang Wen-qian,Li Jiang-bo,Chen Li-ping,Guo Zhi-ming. 2013

[7]Detection of Wheat Powdery Mildew by Differentiating Background Factors using Hyperspectral Imaging. Zhang, Dongyan,Zhang, Lifu,Zhang, Dongyan,Wang, Xiu,Zhang, Dongyan,Wang, Xiu,Lin, Fenfang,Huang, Yanbo. 2016

[8]Rapid determination of biogenic amines in cooked beef using hyperspectral imaging with sparse representation algorithm. Yang, Dong,Lu, Anxiang,Wang, Jihua,Yang, Dong,Wang, Jihua,Lu, Anxiang,Ren, Dong,Wang, Jihua,Lu, Anxiang,Wang, Jihua. 2017

[9]Principles, developments and applications of computer vision for external quality inspection of fruits and vegetables: A review. Zhang, Baohua,Huang, Wenqian,Li, Jiangbo,Zhao, Chunjiang,Fan, Shuxiang,Wu, Jitao,Zhang, Baohua,Zhao, Chunjiang,Liu, Chengliang. 2014

[10]Measuring the Moisture Content in Maize Kernel Based on Hyperspctral Image of Embryo Region. Tian Xi,Huang Wen-qian,Li Jiang-bo,Fan Shu-xiang,Zhang Bao-hua,Tian Xi,Huang Wen-qian,Li Jiang-bo,Fan Shu-xiang,Zhang Bao-hua,Tian Xi,Huang Wen-qian,Li Jiang-bo,Fan Shu-xiang,Zhang Bao-hua,Tian Xi,Huang Wen-qian,Li Jiang-bo,Fan Shu-xiang,Zhang Bao-hua. 2016

[11]Principles and Applications of Hyperspectral Imaging Technique in Quality and Safety Inspection of Fruits and Vegetables. Zhang Bao-hua,Zhang Bao-hua,Li Jiang-bo,Fan Shu-xiang,Huang Wen-qian,Zhang Chi,Wang Qing-yan,Xiao Guang-dong. 2014

[12]Identification of Wheat Cultivars Based on the Hyperspectral Image of Single Seed. Zhu, Dazhou,Wang, Cheng,Wu, Qiong,Zhao, Chunjiang,Pang, Binshuang,Shan, Fuhua. 2012

[13]HYPERSPECTRAL IMAGE FOR DISCRIMINATING APHID AND APHID DAMAGE REGION OF WINTER WHEAT LEAF. Luo Juhua,Huang Wenjiang,Guan Qingsong,Zhao Jinling,Zhang Jingcheng. 2013

[14]Detection of early bruises on peaches (Amygdalus persica L.) using hyperspectral imaging coupled with improved watershed segmentation algorithm. Li, Jiangbo,Chen, Liping,Huang, Wenqian,Li, Jiangbo,Chen, Liping,Huang, Wenqian,Li, Jiangbo,Chen, Liping,Huang, Wenqian,Li, Jiangbo,Chen, Liping,Huang, Wenqian. 2018

[15]Study on the optimal algorithm prediction of corn leaf component information based on hyperspectral imaging. Wu, Qiong,Xu, Tongyu,Wu, Qiong,Wang, Jihua,Wang, Cheng. 2016

[16]Vertical features of yellow rust infestation on winter wheat using hyperspectral imaging measurements. Zhao, Jinling,Zhang, Dongyan,Huang, Linsheng,Zhang, Qing,Liu, Wenjing,Yang, Hao. 2016

[17]Combination of spectral and textural information of hyperspectral imaging for the prediction of the moisture content and storage time of cooked beef. Yang, Dong,Lu, Anxiang,Wang, Jihua,Yang, Dong,Wang, Jihua,He, Dandan,Lu, Anxiang,Ren, Dong,Wang, Jihua,Lu, Anxiang,Wang, Jihua. 2017

[18]Measurement of Light Penetration Depth through Milk Powder Layer in Raman Hyperspectral Imaging System. Liu Chen,Chen Li-ping,Liu Chen,Wang Qing-yan,Huang Wen-qian,Chen Li-ping,Yang Gui-yan,Wang Xiao-bin,Liu Chen,Wang Qing-yan,Huang Wen-qian,Chen Li-ping,Yang Gui-yan,Wang Xiao-bin,Liu Chen,Wang Qing-yan,Huang Wen-qian,Chen Li-ping,Yang Gui-yan,Wang Xiao-bin. 2017

[19]Recognition of wheat preharvest sprouting based on hyperspectral imaging. Wu, Qiong,Wang, Jihua,Wu, Qiong,Zhu, Dazhou,Wang, Cheng,Ma, Zhihong,Wang, Jihua. 2012

[20]Prediction of soluble solids content of apple using the combination of spectra and textural features of hyperspectral reflectance imaging data. Fan, Shuxiang,Zhang, Baohua,Li, Jiangbo,Liu, Chen,Huang, Wenqian,Tian, Xi,Fan, Shuxiang,Zhang, Baohua,Li, Jiangbo,Liu, Chen,Huang, Wenqian,Tian, Xi,Fan, Shuxiang,Zhang, Baohua,Li, Jiangbo,Liu, Chen,Huang, Wenqian,Tian, Xi,Fan, Shuxiang,Zhang, Baohua,Li, Jiangbo,Liu, Chen,Huang, Wenqian,Tian, Xi.

作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：北京市农林科学院

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台