| 基于可见-近红外光谱技术的水稻穗颈瘟染病程度分级方法研究 |
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| 引用本文： | 吴迪,曹芳,张浩,孙光明,冯雷,何勇.基于可见-近红外光谱技术的水稻穗颈瘟染病程度分级方法研究[J].光谱学与光谱分析,2009,29(12). |
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| 作者姓名： | 吴迪 曹芳 张浩 孙光明 冯雷 何勇 |
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| 作者单位： | 1. 浙江大学生物系统工程与食品科学学院,浙江杭州,310029
2. 浙江省农业科学院数字农业研究中心,浙江杭州,310021 |
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| 基金项目： | 国家自然科学基金项目，国家科技支撑项目，浙江省2009年度重大科技专项资助 |
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| 摘 要： | 采用ViS-NIR技术对水稻穗颈瘟染病程度分级方法进行了研究.分别基于原始光谱,变量标准化(SNV)预处理后和多元散射校正(MSC)预处理后的光谱,应用无信息变量消除法(UVE)结合连续投影算法(SPA)对Vis-NIR光谱区进行有效波长的选择.选择后的波长作为输入变量建立最小二乘-支持向量机(LS-SVM)模型.结果表明SNV-UVE-SPA建市的LS-SVM模型预测效果最好.通过SNV-UVE-SPA从全波段600个波长中选择了6个最能够反应光谱信息的波长(459,546,569,590,775和981 nm).SNV-UVE-SPA-LS-SVM组合模型对预测集样本预测得到的确定系数(R_p~2),预测集的预测标准差(RMSEP)和剩余预测偏筹(RPD)分别达到了 0.979,0.507和6.580.结果表明,采用Vis-NIR光谱技术对水稻穗颈瘟染病程度进行分级是可行的.通过UVE-SPA得到的有效波长能够很好地代替全波长.
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| 关 键 词： | Vis-NIR光谱 水稻穗颈瘟 无信息变量消除法 连续投影算法 变量选择 |
Study on Disease Level Classification of Rice Panicle Blast Based on Visible and Near Infrared Spectroscopy |
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| WU Di,CAO Fang,ZHANG Hao,SUN Guang-ming,FENG Lei,HE Yong.Study on Disease Level Classification of Rice Panicle Blast Based on Visible and Near Infrared Spectroscopy[J].Spectroscopy and Spectral Analysis,2009,29(12). |
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| Authors: | WU Di CAO Fang ZHANG Hao SUN Guang-ming FENG Lei HE Yong |
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| Institution: | WU Di1,CAO Fang1,ZHANG Hao2,SUN Guang-ming1,FENG Lei1,HE Yong1 1.College of Biosystems Engineering and Food Science,Zhejiang University,Hangzhou 310029,China2.Digital Agricultural Research Centre,Zhejiang Academy of Agricultural Sciences,Hangzhou 310021,China |
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| Abstract: | Visible and near infrared(Vis-NIR)spectroscopy was used to fast and non-destructively classify the disease levels of rice panicle blast.Reflectance spectra between 325 and 1 075 nm were measured.Kennard-Stone algorithm was operated to separate samples into calibration and prediction sets.Different spectral pretreatment methods,including standard normal variate(SNV)and multiplicative scatter correction(MSC),were used for the spectral pretreatment before further spectral analysis.A hybrid wavelength variable ... |
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| Keywords: | Visible and near infrared (Vis-NIR) spectroscopy Rice panicle blast Uninformative variable elimination (UVE) Successive projections algorithm (SPA) Variable selection |
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