1. Key Laboratory of Modern Precision Agriculture System Integration, Ministry of Education, China Agricultural University, Beijing 100083, China 2. Yunnan Tobacco Company Kunming Branch, Kunming 650051, China 3. Yunnan Academy of Tobacco Agricultural Sciences, Kunming 650021, China
Abstract：Discriminating the maturity levels of tobacco leaf with in-situ measurement can effectively reduce loss rate and quality decline due to misjudgment of the maturity levels of tobacco leaf. In the meantime, the regular way we use to determine the maturity levels of tobacco, which is depend on tobacco leaf age and judgment of tobacco grower, lacks of objectivity. So this paper proposed a method to identify maturity levels of tobacco leaf by using spectral feature parameters combined with the method of support vector machine (SVM). In this paper, a total of 351 tobacco leaf samples collected in 5 maturity levels including immature (M1), unripe (M2), mature (M3), ripe (M4), and mellow (M5) determined by experts were scanned by field spectroscope(ASD FieldSpec3) with in-situ measurement for getting their reflectance spectrum. Through spectral analysis we found that the spectrum of tobacco leaf with different levels of maturity can be distinguished in visible band but not easily be distinguished in near-infrared band, so we use the tobacco leaf spectrum in visible band as the sensitive bands to analyze and model. To find the most suitable input variables for modeling, we use continuous spectrum (350~780 nm), feature band (496~719 nm) and spectral feature parameters (the reflectance of green peak, location of green peak, first order differential value of red-edge and blue-edge, red-edge and blue-edge area, location of red-edge and blue-edge) in visible region as the input variables, and using these three kinds of input variables in the method of SVM to establish a discriminant model for identifying maturity levels of tobacco leaf. The result shows that, the model using spectral feature parameters gains the accuracy rate of 98.85%. While the accuracy rates of other two models were 90.80% and 93.10%, respectively. The conclusion was drawn that using spectral feature parameters in visible spectrum as the input variables in SVM can improve the model performance. It is feasible to use this method to identify maturity level of tobacco leaf with in-situ measurement.
刁 航1，吴永明2，杨宇虹3，欧阳进2，李军会1，劳彩莲1，徐兴阳2*. 田间原位光谱的鲜烟叶成熟度判别模型的研究[J]. 光谱学与光谱分析, 2016, 36(06): 1826-1830.
DIAO Hang1，WU Yong-ming2，YANG Yu-hong3，OUYANG Jin2，LI Jun-hui1，LAO Cai-lian1，XU Xing-yang2*. Study on the Determination of the Maturity Level of Tobacco Leaf Based on In-Situ Spectral Measurement. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2016, 36(06): 1826-1830.
[1] YAN Yan-lu, ZHAO Long-lian, HAN Dong-hai, et al(严衍禄，赵龙莲，韩东海，等). Foundation and Application of NIR Spectra Analysis(近红外光谱分析基础与应用). Beijing: China Light Industry Press(北京： 中国轻工业出版社), 2005. 13. [2] WANG Ren-hong, SONG Xiao-yu, LI Zhen-hai, et al(王仁红，宋晓宇，李振海，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2014, 30(19): 191. [3] QIAO Hong-bo, SHI Yue, SI Hai-ping, et al(乔红波，师 越，司海平，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2014, 30(20): 172. [4] Cruz M V, Sarraguca M C, Freitas F, et al. Journal of Biotechnology, 2015, 194: 1. [5] Wu Yongjiang, Jin Ye, Li Yerui, et al. Vibrational Spectroscopy, 2012, 58: 109. [6] Laroussi-Mezghani S, Vanloot P, Molinet J, et al. Food Chemistry, 2015, 173: 122. [7] Arana V A, Medina J, Alarcon R, et al. Food Chemistry, 2015, 175: 500. [8] TIAN Kuang-da, QIU Kai-xian, LIU Zu-hong, et al(田旷达，邱凯贤，刘祖红，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2014, 34(12): 3262. [9] WANG Jian-wei, ZHANG Yan-ling, LI Hai-jiang, et al(王建伟，张艳玲，李海江，等). Tobacco Science & Technoligy(烟草科技), 2013, (5): 64. [10] YU Zhi-hong, CHEN Jian-jun, Lü Yong-hua, et al(余志虹，陈建军，吕永华，等). Tobacco Science & Technoligy(烟草科技), 2013, 2: 77. [11] YANG Shu-xun(杨树勋). Chinese Tobacco Science(中国烟草科学), 2003, 24(4): 34. [12] Wang Y, Yang M, Wei G, et al. Sensors and Actuators B: Chemical, 2014, 193: 723. [13] Devos O, Downey G, Duponchel L. Food Chemistry, 2014, 148: 124. [14] Burges C J C. Data Mining and Knowledge Discovery, 1998, 2(2): 121. [15] Fu JuiHsi, Lee SingLing. Expert Systems with Applications, 2012, 39(3): 3127.
