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

Identification of Wheat Cultivars Based on the Hyperspectral Image of Single Seed

文献类型： 外文期刊

作者： Zhu, Dazhou 1 ; Wang, Cheng 1 ; Pang, Binshuang 2 ; Shan, Fuhua 2 ; Wu, Qiong 1 ; Zhao, Chunjiang 1 ;

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

2.Beijing Acad Agr & Forestry Sci, Hybrid Wheat Engn & Tech Res Ctr, Beijing 100089, Peoples R China

关键词： Hyperspectral Imaging;Wheat;Seed Cultivar;Single Seed

期刊名称：JOURNAL OF NANOELECTRONICS AND OPTOELECTRONICS （ 影响因子：0.961； 五年影响因子：0.725 ）

ISSN： 1555-130X

年卷期： 2012 年 7 卷 2 期

页码：

收录情况： SCI

摘要： Traditional methods for identification of wheat cultivars involve human inspection, Polyacrylamide gel electrophoresis (PAGE), and reversed phase high performance liquid chromatography (RP-HPLC), and these methods all had some limitations. Therefore, new technologies for identify wheat cultivars for single seed is needed, especially for wheat breeders that usually have small amount of seeds. In this study, hyperspectral imaging was applied to identify wheat cultivars of single seed. hyperspectral image were collected at two seed positions of ventral groove up and ventral groove down, and the mean spectra of this two positions was also calculated. Using image mosaic methods, the average spectra of the single seed region was extracted for analysis. Principle component analysis (PCA) was used to see the characteristic of wheat cultivars in latent space. Soft independent modeling of class analogy (SIMCA) was used to construct the classification model. Results showed that hyperspectral image could characterize the detail feature of single seed, and the data collection position had influence on the hyperspectral image. From the results of PCA and SIMCA model, it indicated that hyperspectral image could differentiate three types of wheat including strong gluten wheat, medium gluten wheat, and weak gluten wheat, the classification accuracy achieved 96%. The classification accuracy of six wheat cultivars including NONGDA195 and ZHONGYOU206 (strong gluten wheat), JINGDONG12 and JINGDONG17 (medium gluten wheat), YANGMAI13 and YANGMAI15 (weak gluten wheat) achieved 93%. For the binary classification of every two cultivars within the same type, the classification accuracy achieved 100%. And all the binary classification accuracy were over 90%. It could be concluded that hyperspectral image was feasible to be used to identify wheat cultivars from single seed.

  • 相关文献

[1]The Classification of Wheat Varieties Based on Near Infrared Hyperspectral Imaging and Information Fusion. Dong Gao,Guo Jian,Dong Gao,Wang Cheng,Chen Zi-long,Zheng Ling,Zhu Da-zhou,Zhu Da-zhou. 2015

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

[3]Differentiation of storage time of wheat seed based on near infrared hyperspectral imaging. Dong Gao,Liang Kehong,Lu Lingang,Wang Jing,Zhu Dazhou,Dong Gao,Guo Jian,Dong Gao,Wang Cheng. 2017

[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]Characteristics of K Nutrient Input/Output and Its Balance for Wheat Season in China. Limin Chuan,Huaiguo Zheng,Cuiping Tan,Sufen Sun,Junfeng Zhang. 2014

[6]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

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

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

[9]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

[10]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

[11]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

[12]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

[13]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

[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]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

[16]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

[17]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

[18]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

[19]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

[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