中国水稻科学

中国水稻科学

• 研究简报 • 上一篇    

基于多光谱图像的水稻叶片叶绿素和籽粒氮素含量检测研究

张浩;姚旭国;张小斌;祝利莉;叶少挺;郑可锋*;胡为群   

  1. 浙江省农业科学院 数字农业研究中心, 浙江 杭州 310021
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2008-09-10 发布日期:2008-09-10

Measurement of Rice Leaf Chlorophyll and Seed Nitrogen Contents by Using Multi-Spectral Imagine

ZHANG Hao; YAO Xu-guo; ZHANG Xiao-bin; ZHU Li-li; YE Shao-ting; ZHENG Ke-feng; HU Wei-qun   

  • Received:1900-01-01 Revised:1900-01-01 Online:2008-09-10 Published:2008-09-10
  • Contact: ZHENG Ke-feng

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被引次数

97

摘要： 先利用常规技术分析了水稻的叶片叶绿素和籽粒氮素含量，然后用包含绿(G)、红(R)和近红外(NIR)三波段通道的电荷耦合器件(CCD)成像技术对水稻叶片和籽粒进行了无损检测。试验结果显示，水稻叶片叶绿素a、叶绿素b分别与G、NIR通道图像灰度呈极显著线性相关，叶绿素(a+b)含量则与上述两通道图像灰度呈显著线性相关；而且，水稻籽粒氮素含量与G、NIR通道、归一化植被指数(NDVI)灰度呈显著线性相关。由此建立了水稻叶片叶绿素和籽粒氮素含量的多光谱图像预测模型，并分别用21个样本对模型进行检验，其中线性显著相关的7个模型的相对误差RE(%)介于9.36%~157%，实现了对水稻叶片叶绿素和籽粒氮素含量的快速、准确、非破坏性检测。

关键词: 多光谱成像, 光谱反射率, 水稻, 植被指数, 遥感

Abstract: To determine rice leaf chlorophyll and seed nitrogen contents,a multi-spectral sensor which assesses the biochemical content of rice by means of gray values sensed using three channels (green,red,near-infrared) of the multi-spectral camera was used. The results showed that there were extremely significant correlations between the chlorophyll a content,chlorophyll b content in leaves and the gray values of green channel,near-infrared channel respectively and significant correlation between the chlorophyll (a+b) content in leaves and the gray values of green channel,near-infrared channel. Similarly,there was a significant correlation between the seed nitrogen content and the gray values of green channel,near-infrared channel and normalized difference vegetation index. Moreover,regression equations between gray values of multi-spectral imagine and leaf chlorophyll content or seed nitrogen content were verified with 21 samples and the relative error of 7 models ranged from 9.36% to 15.7%. ...更多Thus,the rapid,accurate and non-destructive estimations of leaf chlorophyll and seed nitrogen contents were realized.

Key words: multi-spectral imagery, spectra characteristics, rice, vegetation index, remote sensing