| 光谱学与光谱分析 |
|
|
|
|
|
| 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.
|
|
Received: 2014-03-28
Accepted: 2014-07-05
|
|
|
|
Corresponding Authors:
YANG Yu-hong
E-mail: toyyred@263.net
|
|
[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. |
| [1] |
XU Tian1, 2, LI Jing1, 2, LIU Zhen-hua1, 2*. Remote Sensing Inversion of Soil Manganese in Nanchuan District, Chongqing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 69-75. |
| [2] |
LIANG Ye-heng1, DENG Ru-ru1, 2*, LIANG Yu-jie1, LIU Yong-ming3, WU Yi4, YUAN Yu-heng5, AI Xian-jun6. Spectral Characteristics of Sediment Reflectance Under the Background of Heavy Metal Polluted Water and Analysis of Its Contribution to
Water-Leaving Reflectance[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 111-117. |
| [3] |
BAO Hao1, 2，ZHANG Yan1, 2*. Research on Spectral Feature Band Selection Model Based on Improved Harris Hawk Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 148-157. |
| [4] |
LIANG Shou-zhen1, SUI Xue-yan1, WANG Meng1, WANG Fei1, HAN Dong-rui1, WANG Guo-liang1, LI Hong-zhong2, MA Wan-dong3. The Influence of Anthocyanin on Plant Optical Properties and Remote Sensing Estimation at the Scale of Leaf[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 275-282. |
| [5] |
WU Hu-lin1, DENG Xian-ming1*, ZHANG Tian-cai1, LI Zhong-sheng1, CEN Yi2, WANG Jia-hui1, XIONG Jie1, CHEN Zhi-hua1, LIN Mu-chun1. A Revised Target Detection Algorithm Based on Feature Separation Model of Target and Background for Hyperspectral Imagery[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 283-291. |
| [6] |
SHEN Si-cong, ZHANG Jing-xue, CHEN Ming-hui, LI Zhi-wei, SUN Sheng-nan, YAN Xue-bing*. Estimation of Above-Ground Biomass and Chlorophyll Content of
Different Alfalfa Varieties Based on UAV Multi-Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3847-3852. |
| [7] |
HAO Zi-yuan1, YANG Wei1*, LI Hao1, YU Hao1, LI Min-zan1, 2. Study on Prediction Models for Leaf Area Index of Multiple Crops Based on Multi-Source Information and Deep Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3862-3870. |
| [8] |
YANG Wen-feng1, LIN De-hui1, CAO Yu2, QIAN Zi-ran1, LI Shao-long1, ZHU De-hua2, LI Guo1, ZHANG Sai1. Study on LIBS Online Monitoring of Aircraft Skin Laser Layered Paint Removal Based on PCA-SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3891-3898. |
| [9] |
LIANG Ya-quan1, PENG Wu-di1, LIU Qi1, LIU Qiang2, CHEN Li1, CHEN Zhi-li1*. Analysis of Acetonitrile Pool Fire Combustion Field and Quantitative
Inversion Study of Its Characteristic Product Concentrations[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3690-3699. |
| [10] |
CHENG Hui-zhu1, 2, YANG Wan-qi1, 2, LI Fu-sheng1, 2*, MA Qian1, 2, ZHAO Yan-chun1, 2. Genetic Algorithm Optimized BP Neural Network for Quantitative
Analysis of Soil Heavy Metals in XRF[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3742-3746. |
| [11] |
HE Qing-yuan1, 2, REN Yi1, 2, LIU Jing-hua1, 2, LIU Li1, 2, YANG Hao1, 2, LI Zheng-peng1, 2, ZHAN Qiu-wen1, 2*. Study on Rapid Determination of Qualities of Alfalfa Hay Based on NIRS[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3753-3757. |
| [12] |
LIU Wei1, 2, ZHANG Peng-yu1, 2, WU Na1, 2. The Spectroscopic Analysis of Corrosion Products on Gold-Painted Copper-Based Bodhisattva (Guanyin) in Half Lotus Position From National Museum of China[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3832-3839. |
| [13] |
BAI Xue-bing1, 2, SONG Chang-ze1, ZHANG Qian-wei1, DAI Bin-xiu1, JIN Guo-jie1, 2, LIU Wen-zheng1, TAO Yong-sheng1, 2*. Rapid and Nndestructive Dagnosis Mthod for Posphate Dficiency in “Cabernet Sauvignon” Gape Laves by Vis/NIR Sectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3719-3725. |
| [14] |
DANG Rui, GAO Zi-ang, ZHANG Tong, WANG Jia-xing. Lighting Damage Model of Silk Cultural Relics in Museum Collections Based on Infrared Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3930-3936. |
| [15] |
HUANG Meng-qiang1, KUANG Wen-jian2, 3*, LIU Xiang1, HE Liang4. Quantitative Analysis of Cotton/Polyester/Wool Blended Fiber Content by Near-Infrared Spectroscopy Based on 1D-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3565-3570. |
|
|
|
|
