The Study of the Spectral Model for Estimating Pigment Contents of Tobacco Leaves in Field
REN Xiao1, LAO Cai-lian1, XU Zhao-li2, JIN Yan2, GUO Yan3, LI Jun-hui1, YANG Yu-hong2*
1. Key Laboratory of Modern Precision Agriculture System Integration, Ministry of Education, China Agricultural University, Beijing 100083, China 2. Yunnan Academy of Tobacco Agricultural Sciences, Kunming 650021, China 3. College of Resources and Environment, China Agricultural University, Beijing 100193, China
Abstract:Fast and non-destructive measurements of tobacco leaf pigment contents by spectroscopy in situ in the field has great significance in production guidance for nutrient diagnosis and growth monitoring of tobacco in vegetative growth stage,and it is also very important for the quality evaluation of tobacco leaves in mature stage. The purpose of this study is to estimate the chlorophyll and carotenoid contents of tobacco leaves using tobacco leaf spectrum collected in the field. Reflectance spectrum of tobacco leaves in vegetative growth stage and mature stage were collected in situ in the field and the pigment contents of tobacco leaf samples were measured in this study, taking the tobacco leaf samples collected in each and both stages as modeling sets respectively, and using the methods of support vector machine (SVM) and spectral indice to establish the pigment content estimation models, and then compare the prediction performance of the models built by different methods. The study results indicated that the difference of estimation performance by each stage or mixed stages is not significant. For chlorophyll content, SVM and spectral indice modeling methods can both have a well estimation performance, while for carotenoid content, SVM modeling method has a better estimation performance than spectral indice. The coefficient of determination and the root mean square error of SVM model for estimating tobacco leaf chlorophyll content by each stage were 0.867 6 and 0.014 7, while the coefficient of determination and the root mean square error of SVM model for estimating tobacco leaf chlorophyll content by mixed stages were 0.898 6 and 0.012 3; The coefficient of determination and the root mean square error for estimating tobacco leaf carotenoid content by each stage were 0.861 4 and 0.002 5, while the coefficient of determination and the root mean square error of SVM model for estimating tobacco leaf carotenoid content by mixed stages were 0.839 9 and 0.002 5. The innovation point of this study is that on the basis of support vector machine and spectral indice, models established by each stage and mixed stages for estimating the pigment contents of tobacco leaf samples can provide scientific basis and technical support for quality control of tobacco leaf production in field and the ensurance of tobacco leaf recovery quality.
任 晓1,劳彩莲1,徐照丽2,晋 艳2,郭 焱3,李军会1,杨宇虹2* . 估测田间烟叶色素含量的光谱模型研究 [J]. 光谱学与光谱分析, 2015, 35(06): 1654-1659.
REN Xiao1, LAO Cai-lian1, XU Zhao-li2, JIN Yan2, GUO Yan3, LI Jun-hui1, YANG Yu-hong2* . The Study of the Spectral Model for Estimating Pigment Contents of Tobacco Leaves in Field. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2015, 35(06): 1654-1659.
[1] Manetas Y, Grammatikopoulos G, Kyparissis A. Journal of Plant Physiology, 1998, 153(3): 513. [2] Demmig-Adams B, Adams W W. Trends in Plant Science, 1996, 1(1): 21. [3] Havaux M. Trends in Plant Science, 1998, 3(4): 147. [4] WEI Feng-jie, LIU Guo-shun, YANG Yong-feng, etc(韦凤杰,刘国顺,杨永锋,等). Scientia Agricultura Sinica(中国农业科学), 2005, 38(9): 1882. [5] Gitelson A A, Merzlyak M N, Lichtenthaler H K. Journal of Plant Physiology, 1996, 148(3): 501. [6] Gitelson A A, Zur Y, Chivkunova O B, et al. Photochemistry and Photobiology, 2002, 75(3): 272. [7] Wu Chaoyang, Niu Zheng, Tang Quan, et al. Agricultural and Forest Meteorology, 2008, 148(8): 1230. [8] Zude-Sasse M, Truppel I, Herold B. Postharvest Biology and Technology, 2002, 25(2): 123. [9] Liu Meiling, Liu Xiangnan, Li Mi, et al. Biosystems Engineering, 2010, 106(3): 223. [10] Yang Xiaohua, Huang Jingfeng, Wu Yaoping, et al. Science China Life Sciences, 2011, 54(3): 272. [11] Cortes C, Vapnik V. Machine Learning, 1995, 20(3): 273. [12] LI Xiang-yang, LIU Guo-shun, YANG Yong-feng, et al(李向阳,刘国顺,杨永锋,等). Scientia Agricultura Sinica(中国农业科学), 2007, 40(5): 987. [13] LI Fu-lin, ZHAO Chun-jiang, LIU Liang-yun, et al(李佛琳,赵春江,刘良云,等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2006, 22(3): 88. [14] Duan K, Keerthi S S, Poo A N. Neurocomputing, 2003, 51: 41. [15] Muller K-R, Mika S, Ratsch G, et al. Neural Networks, IEEE Transactions on, 2001, 12(2): 181.