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Research on Spectra Recognition Method for Cabbages and Weeds Based on PCA and SIMCA

文献类型： 外文期刊

作者： Zu Qin 1 ; Deng Wei 1 ; Wang Xiu 1 ; Zhao Chun-jiang 1 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Intelligent Equipment Agr, Beijing 100097, Peoples R China

2.Natl Res Ctr Intelligent Equipment Agr, Beijing 100097, Peoples R China

3.Guizhou Univ, Elect Engn Coll, Guiyang 550025, Peoples R China

关键词： Principal component analysis;Feature wavelength;Weed identification;Multiplicative scatter correction;Clustering

期刊名称：SPECTROSCOPY AND SPECTRAL ANALYSIS （ 影响因子：0.589； 五年影响因子：0.504 ）

ISSN： 1000-0593

年卷期： 2013 年 33 卷 10 期

页码：

收录情况： SCI

摘要： In order to improve the accuracy and efficiency of weed identification, the difference of spectral reflectance was employed to distinguish between crops and weeds. Firstly, the different combinations of Savitzky-Golay (SG) convolutional derivation and multiplicative scattering correction (MSC) method were applied to preprocess the raw spectral data. Then the clustering analysis of various types of plants was completed by using principal component analysis (PCA) method, and the feature wavelengths which were sensitive for classifying various types of plants were extracted according to the corresponding loading plots of the optimal principal components in PCA results. Finally, setting the feature wavelengths as the input variables, the soft independent modeling of class analogy (SIMCA) classification method was used to identify the various types of plants. The experimental results of classifying cabbages and weeds showed that on the basis of the optimal pretreatment by a synthetic application of MSC and SG convolutional derivation with SG's parameters set as 1rd order derivation, 3th degree polynomial and 51 smoothing points, 23 feature wavelengths were extracted in accordance with the top three principal components in PCA results. When SIMCA method was used for classification while the previously selected 23 feature wavelengths were set as the input variables, the classification rates of the modeling set and the prediction set were respectively up to 98. 6% and 100%.

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