中国水稻科学

中国水稻科学

• 研究简报 • 上一篇    下一篇

基于学习矢量量化神经网络的水稻白穗和正常穗的高光谱识别

刘占宇1，*;孙华生1;黄敬峰2，*   

  1. 1浙江大学 农业遥感与信息技术应用研究所, 浙江 杭州 310029; 2浙江省农业遥感与信息技术重点实验室, 浙江 杭州 310029
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2007-11-10 发布日期:2007-11-10

Classification of Empty and Healthy Panicles in Rice Plants by Hyperspectral Reflectance Based on Learning Vector Quantization (LVQ) Neural Network

LIU Zhan-yu , SUN Hua-sheng , HUANG Jing-feng   

  • Received:1900-01-01 Revised:1900-01-01 Online:2007-11-10 Published:2007-11-10
  • Contact: HUANG Jing-feng

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被引次数

77

摘要： 水稻病虫害的发生会导致大量白穗的出现，对白穗和正常穗的区分是采取植保措施和灾害评估的基础。通过研究获取了由水稻二化螟和穗瘟造成的白穗和正常穗的室内光谱，选取红边斜率、红边面积、绿峰幅值和绿峰面积等4个高光谱变量作为输入向量，利用学习矢量量化（LVQ）神经网络对水稻白穗和正常穗进行分类。利用测试样本对网络进行测试，结果显示对白穗和正常稻穗的分类精度高达100%。研究表明，基于LVQ神经网络对水稻白穗和正常穗进行辨别的方法是切实可行的，可以补充和替代肉眼观测。

关键词: 水稻, 遥感, 病虫害估测, 高光谱反射率, 学习矢量量化神经网络

Abstract: The incidence of disease and insect stresses in rice brings about numerous empty panicles.Differentiation of empty rice panicles from healthy ones was the basis of plant protection strategies and damage assessment.The hyperspectral reflectance of healthy rice panicles and empty ones caused by Chilo suppressalis(Walker)and rice panicle blast were obtained in the laboratory.Red edge and green peak parameters derived from first-order derivative spectrum were treated as the input vectors of learning vector quantization(LVQ)neural network.Rice panicles were classified into two classes with 30 epochs by LVQ neural network using the training sample(n=108).The performance of LVQ neural network was examined with the testing sample(n=74),and empty and healthy rice panicles could be successfully differentiated without errors.The result demonstrated that the new method was feasible to differentiate empty rice panicles from healthy ones and supplement or substitute the conventional visual survey.

Key words: rice, remote sensing, pest assessment, hyperspectral reflectance, learning vector quantization neural network