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基于叶绿素荧光光谱分析的稻瘟病害预测模型

Rice Blast Prediction Model Based on Analysis of Chlorophyll Fluorescence Spectrum

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摘要

为了实现稻瘟病的快速、 准确和无损检测, 力求构建稻瘟病害预测模型。 根据水稻叶片相对病害面积将稻瘟病划分为3个等级, 通过激光诱导法采集不同病害等级的活体水稻叶片叶绿素荧光光谱。 选取502~830 nm波段激光诱导叶绿素荧光光谱(LICF)作为研究对象, 利用Savitzky-Golay平滑法(SG)和一阶导数变换(FDT)对光谱信息进行预处理, 通过主成分分析(PCA)方法获取经SG-FDT预处理后光谱的特征向量, 根据累积贡献率和方差选取前3个主成分进行分析。 将试验样本分为建模样本和检验样本, 以稻瘟病害等级为预测指标, 利用建模样本的133片叶片的光谱和病害信息分别结合判别分析(DA)、 多类逻辑回归分析(MLRA)和多层感知器(MLP)建立稻瘟病的预测模型, 利用检验样本的89片叶片的光谱和病害信息对所建模型进行预测检验, 完成对PCA-DA、 PCA-MLRA和PCA-MLP的对比寻优。 结果表明, PCA-DA, PCA-MLRA和PCA-MLP模型均能完成对稻瘟病害的预测, 但PCA-MLP模型的平均预测准确率能够达到91.7%, 相比PCA-DA和PCA-MLRA模型, 在稻瘟病害3个等级上均具有较好的分类和预测能力。

Abstract

In order to detect rice blast more rapidly, accurately and nondestructively, the identification and early warning models of rice blast were established in the present research. First of all, rice blast was divided into three grades according to the relative area of disease spots in rice leaf and laser-induced chlorophyll fluorescence spectra of rice leaves at different disease levels were measured in the paddy fields. Meanwhile, 502~830 nm bands of laser-induced chlorophyll fluorescence spectra were selected for the study of rice blast. Savitzky-Golay(SG) smoothing and First Derivative Transform(FDT) were applied for the pretreatment of laser-induced chlorophyll fluorescence spectra. Then the method of Principal Components Analysis(PCA) was used to achieve the dimension reduction on spectral information, three principal components whose variance are greater than 1 and cumulative credibility is 99.924% were extracted by this method. Furthermore, the tentative data were divided into calibration set and validation set, the levels of rice blast were taken as the predictors. Combined with the calibration set which contains the disease and spectral information of 133 leaves, Discriminant Analysis(DA), Multiple Logistic Regression Analysis(MLRA) and Multilayer Perceptron(MLP) were used respectively to establish the identification and early warning models of rice blast. The Prediction examinations of the three models were made based on the validation set which contains the disease and spectral information of 89 leaves. The results show that all the models of PCA-DA, PCA-MLRA and PCA-MLP can carry on the prediction of rice blast, and the average prediction accuracy of PCA-MLP prediction model is 91.7% which is improved compared with PCA- DA and PCA- MLRA.

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中图分类号:O657.3

DOI:10.3964/j.issn.1000-0593(2014)04-1003-04

基金项目:国家高技术研究发展计划(863计划)项目(2012AA10A506-4, 2013AA103005-04), 吉林省科技发展计划项目(20110217)资助

收稿日期:2013-11-20

修改稿日期:2014-02-02

网络出版日期:--

作者单位    点击查看

周丽娜:吉林大学生物与农业工程学院, 仿生工程教育部重点实验室, 吉林 长春 130022长春科技学院, 吉林 长春 130600
于海业:吉林大学生物与农业工程学院, 仿生工程教育部重点实验室, 吉林 长春 130022
张蕾:吉林大学生物与农业工程学院, 仿生工程教育部重点实验室, 吉林 长春 130022
任顺:吉林大学生物与农业工程学院, 仿生工程教育部重点实验室, 吉林 长春 130022
隋媛媛:吉林大学生物与农业工程学院, 仿生工程教育部重点实验室, 吉林 长春 130022
于连军:长春市农业科学院, 吉林 长春 130000

联系人作者:周丽娜(zhoulina976430@163.com)

备注:周丽娜, 1981年生, 吉林大学生物与农业工程学院博士研究生

【1】Neil R B. Annual Review of Plant Biology, 2008, 59: 89.

【2】Yi Jun, HU Yungao, Zhang Ling, et al. International Journal of Environmental Science and Development, 2013, 4(5):582.

【3】LIU Liang-yun, HUANG Mu-yi, HUANG Wen-jiang, et al(刘良云, 黄木易, 黄文江, 等). Journal of Remote Sensing(遥感学报), 2004, 8(3): 275.

【4】Danilo C, Susan S, Mauro J, et al. Environmental and Experimental Botany, 2007, 60(3): 504.

【5】Claus B. Photosynthesis Research, 2007, 92(2): 261.

【6】Barton C V M, North P R J.Remote Sensing of Environment, 2001, 78(3): 264.

【7】CHENG Zhan-hui, LIU Liang-yun(程占慧, 刘良云). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2010, 26(2): 74.

【8】Willits D H, Peet M M. Journal of the American Society for Horticultural Science, 2001, 126(2): 188.

【9】LIANG Liang, ZHANG Lian-peng, LIN Hui, et al(梁亮, 张连蓬, 林卉, 等). Scientia Agricultura Sinica(中国农业科学), 2013, 46(1): 18.

【10】LIU Li-juan, PANG Yong, FAN Wen-yi, et al(刘丽娟, 庞勇, 范文艺, 等). Journal of Remote Sensing(遥感学报), 2013, 17(3): 678.

【11】YU Hai-ye, YANG Hao-yu, ZHANG Lei, et al(于海业, 杨昊谕, 张蕾, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2009, 25(2): 245.

【12】Ndao A S, Konté A, Biaye M, et al. Journal of Fluorescence, 2005, 15(2): 123.

引用该论文

ZHOU Li-na,YU Hai-ye,ZHANG Lei,REN Shun,SUI Yuan-yuan,YU Lian-jun. Rice Blast Prediction Model Based on Analysis of Chlorophyll Fluorescence Spectrum[J]. Spectroscopy and Spectral Analysis, 2014, 34(4): 1003-1006

周丽娜,于海业,张蕾,任顺,隋媛媛,于连军. 基于叶绿素荧光光谱分析的稻瘟病害预测模型[J]. 光谱学与光谱分析, 2014, 34(4): 1003-1006

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