| 光谱学与光谱分析 |
|
|
|
|
|
| Laser Induced Fluorescence Spectrum Characteristics of Paddy under Nitrogen Stress |
| YANG Jian1, SHI Shuo1, 2, 3*, GONG Wei1, 2, DU Lin1, 4, ZHU Bo1, MA Ying-ying1, 2, SUN Jia1 |
1. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079，China
2. Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, China
3. School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China
4. School of Physics and Technology, Wuhan University, Wuhan 430072, China |
|
|
|
|
Abstract Order toguide fertilizing andreduce waste of resources as well as enviro nmental pollution, especially eutrophication, which are caused by excessive fertilization, a system of laser-induced fluorescence(LIF) was built. The system aimed to investigate the correlation between nitrogen(N) content of paddy leaf and the fluorescence intensity. We measuredNcontent and SPAD of paddy leaf (the samples came from the second upper leaves of paddy in tillering stage and the study area was located in Jianghan plain of China) by utilizing the Plant Nutrient (Tester TYS-3N). The fluorescence spectrum was also obtained by using the systembuilt based on theLIFtechnology. Fluorescence spectra of leaf with different N-content were collected and then a fluorescence spectra database wasestablished.It is analyzed that the relationship between the parameters of fluorescence (F740/F685 is the ratio of fluorescence intensity of 740 nm dividing that of 685 nm) and the N level of paddy. It is found that the effect of different N-content on the fluorescence spectrum characteristics is significant. The experiment demonstrated the positive correlation between fluorescence parameters and paddy leaf N-content. Results showed a positive linear correlation between the ratio of peak fluorescence (F740/F685) and N-content. The correlation coefficient (r) reached 0.871 8 and the root mean square error (RMSE) was 0.076 82. The experiment demonstrated that LIF spectroscopy detection technology has the advantages of rapidand non-destructive measurement, and it also has the potential to measure plant content of nutrient elements. It will provide a more accurate remote sensing method to rapidly detect the crop nitrogen levels.
|
|
Received: 2014-12-19
Accepted: 2015-04-14
|
|
|
|
Corresponding Authors:
SHI Shuo
E-mail: shishuo@whu.edu.cn
|
|
[1] LI Fang-lin，HAO Ming-de，YANG Xiao，et al(李芳林，郝明德，杨 晓，等). Journal of Triticeae Crops(麦类作物学报), 2010, 30(1): 154.
[2] WU Jun-hua，YUE Shan-chao，HOU Peng，et al(吴军华，岳善超，侯 鹏，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2011, 31(2): 535.
[3] Martin L Gnyp, Miao Yu-xin, Yuan Fei, et al. Field Crops Research，2014, 155: 42.
[4] Netto A T, Campostrini E, de Oliveira J G, et al.Scientia Horticulturae, 2005, 104(2): 199.
[5] LI Gang-hua，XUE Li-hong，YOU Juan，et al(李刚华，薛利红，尤 娟，等). Scientia Agricultura Sinica(中国农业科学), 2007, 40(6): 1127.
[6] Gunther K P, Dahn H G, Liideker W. Remote Sensing of Environment, 1994, 47: 10.
[7] Saito Y, Takano K, Kobayashi F, et al. Applied Optics, 2014, 53(30): 7030.
[8] Subhash N, Mohanan C N. Remote Sensing of Environment, 1994, 47: 45.
[9] Pan Qi, Rossi G N. Omenetto. Quantum Electronics（量子电子学）, 1990, 7(1): 56.
[10] ZHANG Chong-jing, WU Rong, ZHU Yong-hao, et al(张崇静，吴 荣，朱永豪，等). Remote Sensing of Environment China(中国环境遥感), 1992, 7(Suppl): 34.
[11] McMurtrey Ill J E, Chappelle E W, Kim M S, et al. Remote Sensing of Environment, 1994, 47: 36.
[12] Zarco-Tejada P J, Pushnik J C, Dobrowski S, et al. Remote Sensing of Environment, 2003, 84: 283.
[13] Zivcák M, Olsovská K, Slamka P, et al. Plant Soil and Environment, 2014, 60(5): 210–215. |
| [1] |
QI Guo-min1, TONG Shi-qian1, LIN Xu-cong1, 2*. Specific Identification of Microcystin-LR by Aptamer-Functionalized Magnetic Nanoprobe With Laser-Induced Fluorescence[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3813-3819. |
| [2] |
FENG Hai-kuan1, 2, FAN Yi-guang1, TAO Hui-lin1, YANG Fu-qin3, YANG Gui-jun1, ZHAO Chun-jiang1, 2*. Monitoring of Nitrogen Content in Winter Wheat Based on UAV
Hyperspectral Imagery[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3239-3246. |
| [3] |
KONG De-ming1, LIU Ya-ru1, DU Ya-xin2, CUI Yao-yao2. Oil Film Thickness Detection Based on IRF-IVSO Wavelength Optimization Combined With LIF Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2811-2817. |
| [4] |
HU Wen-feng1, 2, TANG Wei-hao1, LI Chuang1, WU Jing-jin1, MA Qing-fen1, LUO Xiao-chuan1, WANG Chao2, TANG Rong-nian1*. Estimating Nitrogen Concentration of Rubber Leaves Based on a Hybrid Learning Framework and Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2050-2058. |
| [5] |
WANG Shu-ying*, YOU De-chang, MA Wen-jia, YANG Ruo-fan, ZHANG Yang-zhi, YU Zi-lei, ZHAO Xiao-fang, SHEN Yi-fan. Experimental Collisional Energy Transfer Distributions for Collisions of CO2 With Highly Vibrationally Excited Na2[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1760-1764. |
| [6] |
FAN Yi-guang1, 3, 5, FENG Hai-kuan1, 2, 3*, LIU Yang1, 3, 4, LONG Hui-ling1, 3, YANG Gui-jun1, 3, QIAN Jian-guo5. Estimation of Potato Plant Nitrogen Content Based on UAV Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1524-1531. |
| [7] |
FAN Yi-guang1, 3, 5, FENG Hai-kuan1, 2, 3*, LIU Yang1, 3, 4, BIAN Ming-bo1, 3, ZHAO Yu1, 3, YANG Gui-jun1, 3, QIAN Jian-guo5. Estimation of Nitrogen Content in Potato Plants Based on Spectral Spatial Characteristics[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1532-1540. |
| [8] |
YUAN Li1, KONG De-ming2*, CHEN Ji-liang3, ZHONG Mei-yu3, ZHANG Xiao-dan3, XIE Bei-bei3, KONG Ling-fu3. Study on an Equivalent Estimation Method of Oil Spill of Water in Oil
Emulsion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(02): 342-347. |
| [9] |
ZHANG Xiao-dan1, KONG De-ming2*, ZHONG Mei-yu1, MA Qin-yong1, KONG Ling-fu1. Research on an Equivalent Evaluation Algorithm for the Oil Spill Volume of Oil-in-Water Emulsion on the Sea Surface[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(12): 3665-3671. |
| [10] |
YAN Peng-cheng1, 2, ZHANG Xiao-fei2*, SHANG Song-hang2, ZHANG Chao-yin2. Research on Mine Water Inrush Identification Based on LIF and
LSTM Neural Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(10): 3091-3096. |
| [11] |
FAN Yi-guang1, 3, 5, FENG Hai-kuan1, 2, 3*, LIU Yang1, 3, 4, BIAN Ming-bo1, 3, ZHAO Yu1, 3, YANG Gui-jun1, 3, QIAN Jian-guo5. Estimation of Potato Plant Nitrogen Content Using UAV Multi-Source Sensor Information[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(10): 3217-3225. |
| [12] |
YANG Xin1, 2, YUAN Zi-ran1, 2, YE Yin1, 2*, WANG Dao-zhong1, 2, HUA Ke-ke1, 2, GUO Zhi-bin1, 2. Winter Wheat Total Nitrogen Content Estimation Based on UAV
Hyperspectral Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(10): 3269-3274. |
| [13] |
CHEN Si-ying1, JIA Yi-wen1, JIANG Yu-rong1*, CHEN He1, YANG Wen-hui2, LUO Yu-peng1, LI Zhong-shi1, ZHANG Yin-chao1, GUO Pan1. Classification and Recognition of Adulterated Manuka Honey by
Multi-Wavelength Laser-Induced Fluorescence[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(09): 2807-2812. |
| [14] |
YUAN Li1, XIE Bei-bei2, CUI Yong-qiang2, ZHANG Xiao-dan2, JIAO Hui-hui2. Research on Oil Spill Status Recognition Based on LIF[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(07): 2018-2024. |
| [15] |
JI Rong-hua1, 2, ZHAO Ying-ying2, LI Min-zan2, ZHENG Li-hua2*. Research on Prediction Model of Soil Nitrogen Content Based on
Encoder-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1372-1377. |
|
|
|
|
