光谱学与光谱分析 |
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Rice Blast Prediction Model Based on Analysis of Chlorophyll Fluorescence Spectrum |
ZHOU Li-na1, 2, YU Hai-ye1*, ZHANG Lei1, REN Shun1, SUI Yuan-yuan1, YU Lian-jun3 |
1. Key Laboratory of Bionic Engineering, Ministry of Education, School of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China 2. Changchun University of Science and Technology, Changchun 130600, China 3. Changchun City Academy of Agricultural Sciences, Changchun 130000, China |
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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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Received: 2013-11-20
Accepted: 2014-02-02
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Corresponding Authors:
YU Hai-ye
E-mail: haiye@jlu.edu.cn
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[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. |
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