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期刊信息
  • 主管单位:
  • 中国科学技术协会
  • 主办单位:
  • 中国仪器仪表学会、上海光学仪器研究所、中国光学学会工程光学专业委员会
  • 主  编:
  • 庄松林
  • 地  址:
  • 上海市军工路516号上海理工大学《光学仪器》编辑部
  • 邮政编码:
  • 200093
  • 联系电话:
  • 021-55270110
  • 电子邮件:
  • gxyq@usst.edu.cn
  • 国际标准刊号:
  • 1005-5630
  • 国内统一刊号:
  • 31-1504/TH
  • 邮发代号:
  • 单  价:
  • 15.00
  • 定  价:
  • 90.00
马铃薯叶片晚疫病的多光谱分类识别
Classification and identification of late blight disease on potato leaves using multi-spectral imaging technique
投稿时间:2016-07-11  
DOI:10.3969/j.issn.1005-5630.2017.01.003
中文关键词:  多光谱  马铃薯叶片  特征波段  晚疫病  波段指数法
英文关键词:multispectral  the potato leaf  characteristics of band  late blight disease  band index method
基金项目:云南省大学生创新创业训练计划(201510681005)
作者单位E-mail
刘鑫 云南师范大学物理与电子信息学院, 云南 昆明 650500  
冯洁 云南师范大学物理与电子信息学院, 云南 昆明 650500 fengjie_ynnu@126.com 
杨舒明 云南师范大学物理与电子信息学院, 云南 昆明 650500  
摘要点击次数: 3033
全文下载次数: 2947
中文摘要:
      利用Spectrocam多光谱相机获取C-88马铃薯健康叶片和患晚疫病叶片的可见光及近红外通道的多光谱图像。综合考虑多光谱图像各通道间的相关性及其信息量,采用波段指数法选取两种叶片的特征波段,并通过欧氏距离聚类方法对所提取的特征波段进行分类。实验结果表明,用波段指数法提取多光谱图像的特征波段,能快速获得马铃薯叶片的信息,475 nm、558 nm、717 nm、750 nm、850 nm作为马铃薯健康叶片的特征波段,马铃薯患晚疫病叶片的特征波段是509 nm、620 nm、717 nm、750 nm和832 nm。采用欧氏距离法对健康和患病叶片进行识别,其识别率分别可达92.6%和92.8%。因此利用多光谱成像技术可以进行马铃薯病害的快速、准确识别,为实现马铃薯病害的田间实时在线监测提供了参考。
英文摘要:
      This experiment using Spectrocam multispectral camera captured the healthy C-88 potato leaves' and late bright leaves' multispectral image within visible and near infrared bands.Multispectral image correlation between different channels and the amount of information is considered comprehensively.Band index method was used to select the characteristics of the two kinds of leaves,and the Euclidean distance clustering method was used to classify the extracted feature band.The experimental results show that the band index method used to extract the multispectral image bands can quickly obtain the information of potato leaves.We got that 475 nm,558 nm,717 nm,750 nm,850 nm band as healthy leaves of potato characteristics,and the characteristics of the potato late blight cancer leaf wavelength were 509 nm,620 nm,717 nm,750 nm and 832 nm.The recognition rate of healthy and diseased leaves was 92.6% and 92.8% by using the Euclidean distance method.Thus,using multi-spectral imaging technique can rapidly and accurately identify the defect of potato to achieve the real-time online monitoring field of the potato diseases.
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