1. 中国农业大学现代精细农业系统集成研究教育部重点实验室,北京 100083 2. Department of Agricultural and Biological Engineering, University of Florida, Gainesville, Florida, USA
Detection of Citrus Greening Based on Vis-NIR Spectroscopy and Spectral Feature Analysis
MA Hao1, JI Hai-yan1*, Won Suk Lee2
1. Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China 2. Department of Agricultural and Biological Engineering, University of Florida, Gainesville, Florida, USA
Abstract:In the present paper we discussed the methods of classification of citrus greening and extraction of spectral features based on the spectral reflectance of four different statuses of citrus leaves (healthy, HLB, iron deficiency and nitrogen deficiency). Between two classes of classification, the values of discriminability of different spectra were calculated to extract spectral features. The greater value of discriminability showed a bigger difference of the two spectra, which means it would be easier to distinguish the two classes. By the Fisher linear discriminant analysis, three classification models (HLB & healthy, HLB & iron deficiency and HLB & nitrogen deficiency) based on the spectral features yielded more than 90% accuracies, which were better than expected. And at last, we discussed the application of the classification tree in multi-class discriminant analysis and spectral features extraction. The models trained based on the original reflectance spectra, first derivative and selected spectral features yielded more than 88% average accuracy, and especially the model based on the spectral features yielded more than 94% average accuracies, which verified the feasibility of detection of citrus greening in multi-class discriminant analysis and the importance of the spectral feature extraction. The results were compared based on classification tree, k-NN and Bayesian classifiers. Adoption of spectral features as input variables was significantly superior to using the original spectrum, which confirmed the validity of spectral feature selection. Spectral features could be used well for developing a multi-spectral imaging system to detect the citrus greening.
Key words:Citrus greening;Discriminability;Fisher linear discriminant analysis;Classification tree;Vis-NIR spectroscopy
马 淏1,吉海彦1*,Won Suk Lee2 . 基于Vis-NIR光谱的柑橘叶片黄龙病检测及其光谱特性研究 [J]. 光谱学与光谱分析, 2014, 34(10): 2713-2718.
MA Hao1, JI Hai-yan1*, Won Suk Lee2 . Detection of Citrus Greening Based on Vis-NIR Spectroscopy and Spectral Feature Analysis . SPECTROSCOPY AND SPECTRAL ANALYSIS, 2014, 34(10): 2713-2718.
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