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

Hyperspectral imaging combined with multivariate analysis and band math for detection of common defects on peaches (Prunus persica)

文献类型： 外文期刊

作者： Zhang, Baohua 1 ; Li, Jiangbo 1 ; Fan, Shuxiang 1 ; Huang, Wenqian 1 ; Zhao, Chunjiang 1 ; Liu, Chengliang 2 ; Huang, 1 ;

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

2.Shanghai Jiao Tong Univ, State Key Lab Mech Syst & Vibrat, Shanghai 200240, Peoples R China

关键词： Hyperspectral imaging;Multivariate analysis;Band math;Common defects;Peaches

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：5.565； 五年影响因子：5.494 ）

ISSN：

年卷期：

页码：

收录情况： SCI

摘要： Automatic detection of common defects on peaches by using imaging system is still a challenge due to the high variability of peach surface color, the similarity between the defects and stem, as well as the uneven distribution of lightness on peaches. In order to detect the common defects on peaches using hyperspectral imaging, defects were divided into two different types: artificial defects and non-artificial defects. For artificial defect detection, a two-step multivariate analysis method (Monte Carlo-Uninformative Variable Elimination and successful projections algorithm) was conducted in the spectral domain for the discriminant wavelength (DW) selection, and then minimum noise fraction (MNF) transform was conducted on the images at DWs for image processing and artificial defect detection. For the candidate non-artificial defect detection; a pair of two characteristic wavelengths at 925 nm and 726 nm was selected by analyzing the full spectra of sound and non-artificial defective regions, and then a band math equation was constructed for differentiating the non-artificial defect regions and stems from the sound and physical damage regions, and the candidate non-artificial defects (including non-artificial defects and stems) could be segmented by using a simple threshold method. In order to distinguish the stem from the segmented candidate non-artificial defect regions, another band math equation was constructed based on another pair of two characteristic wavelengths at 650 nm and 675 nm for stem identification. Additionally, the uneven lightness distribution in the spectral images was also investigated and eliminated by the band math methods. The overall classification accuracy of 93.3% for the 120 samples indicated that the selected DWs and proposed method were suitable and efficient for the common defect detection. The limitation of our research is the static inspection in one single view. (C) 2015 Elsevier B.V. All rights reserved.

  • 相关文献

[1]Metschnikowia persici sp nov., A Novel Protease-Producing Yeast Species from China. Wang, Chen,Liu, Ya,Wang, Chen,Zhang, Tao-tao,Lu, Cai-ge,Zhang, Dian-peng,Liu, Wei-cheng,Liu, Yang.

[2]Multivariate and geostatistical analyses of the spatial distribution and origin of heavy metals in the agricultural soils in Shunyi, Beijing, China. Lu, Anxiang,Zhang, Shuzhen,Lu, Anxiang,Wang, Jihua,Han, Ping,Qin, Xiangyang,Wang, Kaiyi.

[3]Detection of Early Rottenness on Apples by Using Hyperspectral Imaging Combined with Spectral Analysis and Image Processing. Zhang, Baohua,Fan, Shuxiang,Li, Jiangbo,Huang, Wenqian,Zhao, Chunjiang,Qian, Man,Zheng, Ling,Zhang, Baohua,Zhao, Chunjiang.

[4]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.

[5]Multispectral detection of skin defects of bi-colored peaches based on vis-NIR hyperspectral imaging. Li, Jiangbo,Chen, Liping,Huang, Wenqian,Wang, Qingyan,Zhang, Baohua,Tian, Xi,Li, Bin,Li, Jiangbo,Chen, Liping,Huang, Wenqian,Wang, Qingyan,Tian, Xi,Fan, Shuxiang,Li, Bin,Li, Jiangbo,Chen, Liping,Huang, Wenqian,Li, Jiangbo,Chen, Liping,Huang, Wenqian.

[6]Development of a multispectral imaging system for online detection of bruises on apples. Huang, Wenqian,Li, Jiangbo,Wang, Qingyan,Chen, Liping.

[7]Prediction of Soluble Solids Content and Firmness of Pears Using Hyperspectral Reflectance Imaging. Fan, Shuxiang,Huang, Wenqian,Guo, Zhiming,Zhang, Baohua,Zhao, Chunjiang,Fan, Shuxiang,Zhao, Chunjiang.

[8]Hyperspectral classification for identifying decayed oranges infected by fungi. Yin, Shiyang,Gu, Xiaomin,Xiao, Yong,Bi, Xiaoqing,Niu, Yong. 2017

[9]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

[10]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

[11]Geographical classification of apple based on hyperspectral imaging. Guo, Zhiming,Huang, Wenqian,Chen, Liping,Zhao, Chunjiang. 2013

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

[13]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

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

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

[16]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

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

[18]Machine vision technology for detecting the external defects of fruits - a review. Li, J. B.,Huang, W. Q.,Zhao, C. J.. 2015

[19]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

[20]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

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