光谱学与光谱分析 |
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Research on Discrimination of Cabbage and Weeds Based on Visible and Near-Infrared Spectrum Analysis |
ZU Qin1, 2, 3, ZHAO Chun-jiang1, 3, DENG Wei1, 3*, WANG Xiu1, 3 |
1. Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China 2. The Electrical Engineering College of Guizhou University, Guiyang 550025, China 3. Beijing Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China |
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Abstract The automatic identification of weeds forms the basis for precision spraying of crops infest. The canopy spectral reflectance within the 350~2 500 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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Received: 2012-07-30
Accepted: 2012-10-30
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Corresponding Authors:
DENG Wei
E-mail: dengw@nercita.org.cn
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[1] CHEN De-run, WANG Xiu, WANG Shu-mao(陈德润,王 秀,王书茂). Chinese Agricultural Mechanization(中国农机化), 2005, 2: 35. [2] Piron A, Leemans V, Kleynen O. Computers and Electronics in Agriculture, 2008, 62: 141. [3] Exposito M J, Granados F L, Atenciano S. Crop Protection, 2003, 22: 1177. [4] Koger C H, Bruce L M, Shaw D R. Remote Sensing of Environment, 2003, 86(1): 108. [5] LI Xian-feng, ZHU Wei-xing, JI Bin(李先锋,朱伟兴,纪 滨). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报),2010, 41(11): 168. [6] CHU Xiao-li, YUAN Hong-fu, LU Wan-zhen(褚小立,袁洪福,陆婉珍). Progress in Chemistry(化学进展),2004, 16(4): 528. [7] CHEN Hua-zhou, PAN Tao, CHEN Jie-mei(陈华舟,潘 涛,陈洁梅). Computers and Applied Chemistry(计算机与应用化学),2011, 28(5): 518. [8] CHU Xiao-li(褚小立). Molecular Spectroscopy Analytical Technology Combined with Chemometrics and its Applications(化学计量学方法与分子光谱分析技术). Beijing: Chemical Industry Press(北京:化学工业出版社),2011. 21. [9] LI Juan, FAN Lu, BI Yan-lan(李 娟,范 璐,毕艳兰). Chinese Journal of Analytical Chemistry(分析化学),2010, 38(4): 475. |
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