Diagnosis of Cucumber Diseases and Insect Pests by Fluorescence Spectroscopy Technology Based on PCA-SVM
YANG Hao-yu1, YU Hai-ye1*, LIU Xu1,2, ZHANG Lei1, SUI Yuan-yuan1
1. Key Laboratory of Bionic Engineering, Ministry of Education, School of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China 2. Department of Applied Mathematics of Changchun Taxation College, Changchun 130117, China
Abstract：The diagnosis model of the cucumber diseases and insect pests was established by laser-induced chlorophyll fluorescence (LICF) spectroscopy technology combined with support vector machines (SVM) algorithm in the present research. This model would be used to realize the fast and exact diagnosis of the cucumber diseases and insect pests. The noise of original spectrum was reduced by three methods, including Savitzky-Golay smoothing (SG), Savitzky-Golay smoothing combined with fast Fourier transform (FFT) and Savitzy-Golay smoothing combined with first derivative transform (FDT). According to the accumulative reliabilities (AR) seven principal components (PCs) were selected to replace the complex spectral data. The one hundred fifty samples were randomly separated into the calibration set and the validation set. Support vector machines (SVM) algorithm with four kinds of kernel functions was used to establish diagnosis models of the cucumber diseases and insect pests based on the calibration set, then these models were applied to the diagnosis of the validation set. According to the best diagnosis accuracy of cross-validation method in calibration set, the parameters of four kinds of kernel function models were optimized, and the capabilities of SVM with different kernel function were compared. Results showed that SVM with the ploy kernel function had the best identification capabilities and the accuracy was 98.3% after the original spectrum noise was reduced by SG+FDT+PCA. This research indicated that the method of PCA-SVM had a good identification effect and could realize rapid diagnosis of the cucumber diseases and insect pests as a new method.
杨昊谕1，于海业1*，刘 煦1,2，张 蕾1，隋媛媛1 . 叶绿素荧光PCA-SVM分析的黄瓜病虫害诊断研究 [J]. 光谱学与光谱分析, 2010, 30(11): 3018-3021.
YANG Hao-yu1, YU Hai-ye1*, LIU Xu1,2, ZHANG Lei1, SUI Yuan-yuan1 . Diagnosis of Cucumber Diseases and Insect Pests by Fluorescence Spectroscopy Technology Based on PCA-SVM . SPECTROSCOPY AND SPECTRAL ANALYSIS, 2010, 30(11): 3018-3021.
[1] Vapnik V N. The Nature of Statistical Learning Theory. New York: Springer-Verlag, 1955. [2] Cao Y Y, Frank P M. IEEE Trans on Fuzzy Systems, 2000, 8(2): 200. [3] ZHANG Xiang, TIAN Jin-wen, XIAO Xiao-ling, et al(张 翔，田金文，肖晓玲，等). Signal Processing(信号处理), 2004, 20(2): 208. [4] Rober E K, David J G, Grant R G. Society of Photo-Optical Instrum Entation Engineers, 2000, 39(3): 704. [5] PU Rui-liang, GONG Peng(蒲瑞良，宫 鹏). High-Spectrum Remote Sensing and Application(高光谱遥感及其应用). Beijing: Higher Education Press(北京：高等教育出版社), 2000. [6] YAN Yan-lu, ZHAO Long-lian, HAN Dong-hai, et al(严衍禄，赵龙莲，韩东海，等). Foundation of Near Infrared Spectral Analysis and Its Application(近红外光谱分析基础与应用). Beijing: China Light Industry Press(北京：中国轻工业出版社), 2005. [7] WANG Li, HE Yong, LIU Fei, et al(王 莉，何 勇，刘 飞, 等). Journal of Infrared and Millimeter Waves(红外与毫米波学报), 2008, 27(1): 51. [8] Hartley R D. Phytochemistry, 1973, 12: 305. [9] Harris P J, Hartely R D. Nature, 1976, 259: 508. [10] Lichtenthaler H K, Schweiger J. Journal of Plant Physiology, 1998, 152: 272. [11] Pfudel E. Photosynth Research, 1988, 56: 185.
