Analysis of Tobacco Color and Location Features Using Visible-Near Infrared Hyperspectral Data
CAI Jia-yue1, LIANG Miao1, WEN Ya-dong2, YU Chun-xia2, WANG Luo-ping2, WANG Yi2, ZHAO Long-lian1, LI Jun-hui1*
1. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China 2. Technology Center of Yunnan Tobacco Industry Co., Ltd., Kunming 650231, China
Abstract：In the present paper, six categories of standard industrial grading tobacco provided by Hongta Group are taken as experimental samples, including three different tobacco locations-upper (B), middle(C) and lower(X) parts, with each part containing two kinds of tobacco colors-orange (O) and lemon yellow (L). Two methods including projection model method based on principal component and Fisher criterion (PPF) and support vector machine (SVM) method are used to analyze color and location features of tobacco based on visible-near infrared hyperspectral data. The results of projection model method indicate that in the projection and similarity analysis of tobacco color, location and six tobacco groups classified by color and location, two kinds of color can be fully differentiated, of which the similarity value is -1.000 8. Tobacco from upper and lower parts can also be fully differentiated with similarity value 0.405 3, but they both have intersections with tobacco from middle part. Six tobacco groups classified by color and location can be fully differentiated as well and their projection positions meet the actual external features of tobacco. The results of support vector machine method indicate that in the discriminant analysis of tobacco color, location and six tobacco groups classified by color and location, the average recognition rate of tobacco colors reaches 98%. The average recognition rate of tobacco location is 96%. The average recognition rate of six tobacco groups is 94%. Therefore, it’s feasible to analyze color and location features of tobacco using visible-near infrared hyperspectral data, which can provide reference for tobacco quality evaluation, computer-aided grading and tobacco intelligent acquisition, and also offers a new approach to the analysis of exterior features of other agricultural products.
Key words：Visible-near infrared hyperspectral data;Tobacco;Color features;Location features
蔡嘉月1，梁 淼1，温亚东2，于春霞2，王萝萍2，王 毅2，赵龙莲1，李军会1* . 应用可见-近红外高光谱分析烟叶的颜色和部位特征 [J]. 光谱学与光谱分析, 2014, 34(10): 2758-2763.
CAI Jia-yue1, LIANG Miao1, WEN Ya-dong2, YU Chun-xia2, WANG Luo-ping2, WANG Yi2, ZHAO Long-lian1, LI Jun-hui1*. Analysis of Tobacco Color and Location Features Using Visible-Near Infrared Hyperspectral Data. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2014, 34(10): 2758-2763.
[1] DING Gen-sheng, ZHANG Qing-ming, BA Jin-sha. Chinese Tobacco Science, 2011, 32(4)：14. [2] YAN Ke-yu, ZHAO Ming-qin. Tobacco Raw Material. Beijing：Science Press, 2008. 372. [3] YU Chunxia, MA Xiang, ZHANG Yehui, et al. Spectroscopy and Spectral Analysis, 2011, 31(4)：924. [4] Ni Lijun, Zhang Liguo, Xie Juan, et al. Analytica Chimica Acta, 2009, 633(1)：53. [5] ZHANG Jianping, CHEN Jianghua, SHU Ruxin, et al. Acta Tabacaria Sinica, 2007, 13(5)：1. [6] Bierwirth P, Huston D, Blewett R. Economic Geology, 2002, 97: 819. [7] Yu Keqiang, Zhao Yanru, Li Xiaoli. Computers and Electronics in Agriculture, 2014，103：1. [8] Gaia VaglioLaurin, Qi Chen, Jeremy A Lindsell. ISPRS Journal of Photogrammetry and Remote Sensing, 2014，89：49. [9] HONG Tian-sheng, LIZhen, WU Chun-yin. Transactions of the CSAE，2007, 23(11): 280. [10] Noomen M F, Skidmore A K, Van der Meer F D. Proceedings of 3rd EARSeL Workshop on Imaging Spectroscopy，2003. 252. [11] YAN Yan-lu, ZHAO Long-lian, HAN Dong-hai, et al. Foundation and Application of NIR Spectra Analysis. Beijing: China Light Industry Press, January, 2005. [12] WEN Ya-dong, WANG Yi, WANG Neng-ru. Acta Tabacaria Sinica，2009，15(5). [13] LI Guo-zheng, WANG Meng, ZENG Hua-jun, trans. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Beijing: Electronics Industry Press, 2004. [14] CarlottaFerrari, Giorgia Foca, Alessandro Ulrici. Analytica Chimica Acta, 2013，802：29. [15] Piotr Baranowski, Wojciech Mazurek, Joanna Pastuszka-Wo′zniak. Postharvest Biology and Technology, 2013 86：249.
