光谱学与光谱分析 |
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Chlorophyll Fluorescence Spectrum Analysis of Greenhouse Cucumber Disease and Insect Damage |
SUI Yuan-yuan, YU Hai-ye*, ZHANG Lei, LUO Han, REN Shun, ZHAO Guo-gang |
Key Laboratory of Bionic Engineering, Ministry of Education, School of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China |
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Abstract The present paper is based on chlorophyll fluorescence spectrum analysis. The wavelength 685 nm was determined as the primary characteristic point for the analysis of healthy or disease and insect damaged leaf by spectrum configuration. Dimensionality reduction of the spectrum was achieved by combining simple intercorrelation bands selection and principal component analysis (PCA). The principal component factor was reduced from 10 to 5 while the spectrum information was kept reaching 99.999%. By comparing and analysing three modeling methods, namely the partial least square regression (PLSR), BP neural network (BP) and least square support vector machine regression (LSSVMR), regarding correlation coefficient of true value and predicted value as evaluation criterion, eventually, LSSVMR was confirmed as the appropriate method for modeling of greenhouse cucumber disease and insect damage chlorophyll fluorescence spectrum analysis.
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Received: 2011-08-21
Accepted: 2011-11-28
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Corresponding Authors:
YU Hai-ye
E-mail: haiye@jlu.edu.cn
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[1] Ming L, Chunjian Z, Daoliang L, et al. Computer and Computing Technologies in Agriculture, 2008, 2(259): 1375. [2] WU Peng, QIN Zhi-wei, ZHOU Xiu-yan, et al(吴 鹏,秦智伟,周秀艳,等). Journal of Northeast Agricultural University(东北农业大学学报), 2011, 42(1): 138. [3] XU Hui-rong(徐惠荣). Development and Application of Optimal Medel for Nondestructive Evaluation of Fruits Suger Content Using Visible/Near Infrared Spectroscopy(基于可见近红外光谱的水果糖度检测模型优化及应用研究). Hangzhou: Zhejiang University Press(杭州: 浙江大学出版社), 2010. [4] YUE Tian-li, PENG Bang-zhu, YUAN Ya-hong, et al(岳田利,彭帮柱,袁亚宏,等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2007, 23(6): 223. [5] LI Jing-ping, XIE Bang-chang(李静萍,谢邦昌). Multivarate Analysis: Methods and Application(多元统计分析: 方法与应用). Beijing:China Renmin University Press (北京:中国人民大学出版社), 2008. [6] Thenkabail P S, Enclona E A, Ashton M S, et al. Remote Sensing of Environment, 2004, 91: 354. [7] LI Shi-xin, WEN Jian, SHAO Xiao-hou, et al(李世欣,温 建,邵孝侯,等). Journal of Hohai University(Natural Sciences)(河海大学学报·自然科学版), 2010, 38(2): 149. [8] ZHAO Guo-fu, ZHAO Peng(赵国富,赵 朋). Journal of Agricultural Mechanization Research(农机化研究), 2008, 4: 14,28. [9] Cortes C, Vapnik V. Machine Learning, 1995, 20(3): 273. [10] Vapnik V N. The Nature of Statistical Learning Theory. New York: Springer-Verlag, 1955. [11] Cristianini N, Shawe-Taylor J. An Introduction to Support Vector Machines and other Kerner-Based Learning Methods(支持向量机导论). Beijing: Publishing House of Electronics Industry(北京:电子工业出版社), 2004. [12] DENG Nai-yang, TIAN Ying-jie(邓乃扬,田英杰). New Method of Data Minning Support Vector Machine(数据挖掘中的新方法——支持向量机). Beijing: Science Press(北京: 科学出版社), 2004. [13] WEI Hui-min, ZHANG Nian-hui, DU Lin-fang(魏慧敏,张年辉,杜林方). Journal of Sichuan University(Natural Science Edition)(四川大学学报·自然科学版), 2004, 41(5): 1059.
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