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Research on Discrimination of Cabbage and Weeds Based on Visible and Near-Infrared Spectrum Analysis

文献类型： 外文期刊

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

作者机构： 1.Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

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

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

关键词： Weeds discrimination;Visible and near-infrared;Principal component analysis;Multiple scattering correction

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 5 期

页码：

收录情况： SCI

摘要： The automatic identification of weeds forms the basis for precision spraying of crops infest. The canopy spectral reflectance within the 350 similar to 2 500 nm band of two strains of cabbages and five kinds of weeds such as barnyard grass, setaria, crabgrass, goosegrass and pigweed was acquired by ASD spectrometer. According to the spectral curve characteristics, the data in different bands were compressed with different levels to improve the operation efficiency. Firstly, the spectrum was denoised in accordance with the different order of multiple scattering correction (MSC) method and Savitzky-Golay (SG) convolution smoothing method set by different parameters, then the model was built by combining the principal component analysis (PCA) method to extract principal components, finally all kinds of plants were classified by using the soft independent modeling of class analogy (SIMCA) taxonomy and the classification results were compared. The tests results indicate that after the pretreatment of the spectral data with the method of the combination of MSC and SG set with 3rd order, 5th degree polynomial, 21 smoothing points, and the top 10 principal components extraction using PCA as a classification model input variable, 100% correct classification rate was achieved, and it is able to identify cabbage and several kinds of common weeds quickly and nondestructively.

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