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Journal of Zhejiang University-SCIENCE B (Biomedicine & Biotechnology)  2010, Vol. 11 Issue (1): 71-78    DOI: 10.1631/jzus.B0900193
Biotechnology     
Discrimination of rice panicles by hyperspectral reflectance data based on principal component analysis and support vector classification
Zhan-yu LIU, Jing-jing SHI, Li-wen ZHANG, Jing-feng HUANG
Institute of Agricultural Remote Sensing and Information System Application, Zhejiang University, Hangzhou 310029, China; Ministry of Education Key Laboratory of Environmental Remediation and Ecological Health, Zhejiang University, Hangzhou 310029, China; Key Laboratory of Agricultural Remote Sensing and Information System in Zhejiang Province, Hangzhou 310029, China
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Abstract  Detection of crop health conditions plays an important role in making control strategies of crop disease and insect damage and gaining high-quality production at late growth stages. In this study, hyperspectral reflectance of rice panicles was measured at the visible and near-infrared regions. The panicles were divided into three groups according to health conditions: healthy panicles, empty panicles caused by Nilaparvata lugens Stål, and panicles infected with Ustilaginoidea virens. Low order derivative spectra, namely, the first and second orders, were obtained using different techniques. Principal component analysis (PCA) was performed to obtain the principal component spectra (PCS) of the foregoing derivative and raw spectra to reduce the reflectance spectral dimension. Support vector classification (SVC) was employed to discriminate the healthy, empty, and infected panicles, with the front three PCS as the independent variables. The overall accuracy and kappa coefficient were used to assess the classification accuracy of SVC. The overall accuracies of SVC with PCS derived from the raw, first, and second reflectance spectra for the testing dataset were 96.55%, 99.14%, and 96.55%, and the kappa coefficients were 94.81%, 98.71%, and 94.82%, respectively. Our results demonstrated that it is feasible to use visible and near-infrared spectroscopy to discriminate health conditions of rice panicles.

Key wordsRice panicle      Principal component analysis (PCA)      Support vector classification (SVC)      Hyperspectral reflectance      Derivative spectra     
Received: 06 July 2009      Published: 01 January 2010
CLC:  TP7  
  S43  
Cite this article:

Zhan-yu LIU, Jing-jing SHI, Li-wen ZHANG, Jing-feng HUANG. Discrimination of rice panicles by hyperspectral reflectance data based on principal component analysis and support vector classification. Journal of Zhejiang University-SCIENCE B (Biomedicine & Biotechnology), 2010, 11(1): 71-78.

URL:

http://www.zjujournals.com/xueshu/zjus-b/10.1631/jzus.B0900193     OR     http://www.zjujournals.com/xueshu/zjus-b/Y2010/V11/I1/71

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