光谱学与光谱分析 |
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Variety Recognition of Chinese Cabbage Seeds by Hyperspectral Imaging Combined with Machine Learning |
CHENG Shu-xi, KONG Wen-wen, ZHANG Chu, LIU Fei*, HE Yong |
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China |
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Abstract The variety of Chinese cabbage seeds were recognized using hyperspectral imaging with 256 bands from 874 to 1 734 nm in the present paper. A total of 239 Chinese cabbage seed samples including 8 varieties were acquired by hyperspectral image system, 158 for calibration and the rest 81 for validation. A region of 15 pixel×15 pixel was selected as region of interest (ROI) and the average spectral information of ROI was obtained as sample spectral information. Multiplicative scatter correction was selected as pretreatment method to reduce the noise of spectrum. The performance of four classification algorithms including Ada-boost algorithm, extreme learning machine (ELM), random forest (RF) and support vector machine (SVM) were examined in this study. In order to simplify the input variables, 10 effective wavelengths (EMS) including 1 002,1 005,1 015,1 019,1 022,1 103,1 106,1 167,1 237 and 1 409 nm were selected by analysis of variable load distribution in PLS model. The reflectance of effective wavelengths was taken as the input variables to build effective wavelengths based models. The results indicated that the classification accuracy of the four models based on full-spectral were over 90%, the optimal models were extreme learning machine and random forest, and the classification accuracy achieved 100%. The classification accuracy of effective wavelengths based models declined slightly but the input variables compressed greatly, the efficiency of data processing was improved, and the classification accuracy of EW-ELM model achieved 100%. ELM performed well both in full-spectral model and in effective wavelength based model in this study, it was proven to be a useful tool for spectral analysis. So rapid and nondestructive recognition of Chinese cabbage seeds by hyperspectral imaging combined with machine learning is feasible, and it provides a new method for on line batch variety recognition of Chinese cabbage seeds.
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Received: 2013-07-17
Accepted: 2014-02-16
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Corresponding Authors:
ZHANG Chu, LIU Fei
E-mail: fliu@zju.edu.cn
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[1] Singh C B, Jayas D S, Paliwal J, et al. Comput. Electron. Agric.,2010, 73(2): 118. [2] Jain N R, Singh S S, Panigrahy S. Precis. Agric.,2007, 8(4-5): 225. [3] FENG Zhao-li, ZHU Qi-bing, ZHU Xiao, et al(冯朝丽, 朱启兵, 朱 晓, 等). Journal of Jiangnan University·Natural Science Edition(江南大学学报·自然科学版), 2012, 11(2): 149. [4] TAN Liang-kui, YANG Qin, WANG Wen-kai(谭亮魁, 杨 琴, 王文凯). Chinese Agricultural Science Bulletin(中国农学通报), 2012, 28(33): 118. [5] LI Qiao-yun, ZHANG Zhi-gang, LIU Shuan-tao, et al(李巧云, 张志刚, 刘栓桃, 等). Acta Agriculture Boreali-Sinica(华北农学报), 2012, 27(4): 135. [6] LI Li, ZHENG Xiao-ying(李 丽, 郑晓鹰). Acta Horticulturae Sinica(园艺学报), 2009, 36(11): 1627. [7] Geladi P, MacDougall D, Martens H. Appl. Spectrosc.,1985, 39(3): 491. [8] Helland I S, Ns T, Isaksson T. Chemometrics Intell. Lab. Syst.,1995, 29(2): 233. [9] Freund Y, Schapire R E J. Comput. Syst. Sci.,1997, 55(1): 119. [10] ZHU Chao-ping(朱超平). Journal of Chongqing Technology and Business·Natural Sciences Edition(重庆工商大学学报·自然科学版), 2011, 28(4): 390. [11] Huang G B, Zhu Q Y, Siew C K. Neurocomputing, 2006, 70(1-3): 489. [12] Lan Y, Soh Y C, Huang G B. Neurocomputing, 2009, 72(13-15): 3391. [13] Breiman L. Mach. Learn. 2001, 45(1): 5. [14] FANG Kuang-nan, WU Jian-bin, ZHU Jian-ping,et al(方匡南, 吴见彬, 朱建平, 等). Statistics & Information Forum(统计信息论坛), 2011, 26(3): 32. [15] WU Xiao-yan, LI Kang(武晓岩, 李 康). Chinese Journal of Health Statistics(中国卫生统计),2009, 26(4): 437. [16] ZHENG Li-hua, LI Min-zan, AN Xiao-fei, et al(郑立华, 李民赞, 安晓飞, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2010, 26(2): 81. [17] CHU Xiao-li(褚小立). Molecular Spectroscopy Analytical Technology Combined with Chemometrics and Its Application(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. [18] Workman J, Weyer L. Practical Guide to Interpretive Near-Infrared Spectroscopy. Boca Raton: CRC Press, 2007. |
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