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中国农学通报

中国农学通报 ›› 2017, Vol. 33 ›› Issue (17): 117-122.doi: 10.11924/j.issn.1000-6850.casb17030124

所属专题： 水稻

• 工程 机械 水利 装备 • 上一篇    下一篇

稻瘟病胁迫下水稻叶片叶绿素含量与光谱特征参数的相关性研究

谢 凯1,蒋 蘋1,2,罗亚辉1   

  1. (1湖南农业大学工学院,长沙 410128；2南方粮油作物协同创新中心,长沙 410128）
  • 收稿日期:2017-03-20 修回日期:2017-05-15 接受日期:2017-04-07 出版日期:2017-06-16 发布日期:2017-06-16
  • 通讯作者: 谢凯
  • 基金资助:
    湖南省科技计划项目“油茶籽产地商品化处理关键技术研究与装备开发”(2016NK2117)；湖南省教育厅科研项目“基于图像识别和微质量检测的油茶籽分级技术研究”(15C0664)。

Correlation Between Chlorophyll Content and SpectralCharacteristics of Rice Leaves Under Rice Blast

Xie Kai1, Jiang Ping1,2, Luo Yahui1   

  1. (1College of Engineering, Hunan Agricultural University, Changsha 410128;2Collaborative Innovation Center of Southern Chinese Grain and Oilseed, Changsha 410128)
  • Received:2017-03-20 Revised:2017-05-15 Accepted:2017-04-07 Online:2017-06-16 Published:2017-06-16

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摘要： 为实现受稻瘟病侵染水稻叶片叶绿素含量的高光谱反演，以‘陵两优268’为研究对象，测定受稻瘟病侵染的85个水稻叶片样品的叶绿素含量和高光谱反射率，分析受稻瘟病侵染的水稻叶片高光谱反射率与叶绿素含量间的相关关系，使用线性与非线性回归技术建立叶绿素含量反演模型。结果显示：叶绿素含量与原始光谱及一阶导数光谱的敏感波段分别发生在700 nm和752 nm，基于光谱特征参数SDr的回归模型均方根误差为1.27，平均相对误差为10.2%。研究表明受稻瘟病侵染水稻叶片光谱反射率差异明显，基于光谱特征参数SDr的回归模型预测叶绿素含量具有较高的精度。

关键词: 污泥堆肥, 污泥堆肥, 变性梯度凝胶电泳, 高通量测序

Abstract: In order to realize the hyperspectral inversion of chlorophyll content in rice leaves infected with rice blast,the chlorophyll content and hyperspectral reflectance of 85 rice leaf samples infected by rice blast were measured and analyzed. The correlation between the hyperspectral reflectance and the chlorophyll content of rice leaves infected by rice blast was also analyzed. The linear and non-linear regression techniques were then used to establish the chlorophyll content of the inversion model.The results showed that the sensitivity of the chlorophyll content to both the original spectra and the first derivative spectra occurred at 700 nm and 752 nm respectively. The regression model was based on the spectral characteristic parameter SDr had a mean square error of 1.27 and an average relative error of 10.2 %.The results showed that the spectral reflectance of rice leaves infected with rice blast was significantly different. Based on the regression model and on the spectral characteristic parameter SDr, the chlorophyll content was predicted to be high.