Plant Soil Environ., 2017, 63(9):396-401 | DOI: 10.17221/460/2017-PSE

Application of the chlorophyll fluorescence ratio in evaluation of paddy rice nitrogen statusOriginal Paper

Jian YANG*,1,2, Lin DU1, Wei GONG*,2,3, Jia SUN2, Shuo SHI2,3, Biwu CHEN2
1 Faculty of Information Engineering, China University of Geosciences, Wuhan, Hubei, P.R. China
2 State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, Hubei, P.R. China
3 Collaborative Innovation Centre of Geospatial Technology, Wuhan, Hubei, P.R. China

In this research, laser-induced fluorescence (LIF) technique combined with back-propagation neural network (BPNN) was employed to analyse different nitrogen (N) fertilization levels in paddy rice. Leaf fluorescence characteristics (FLCs) were measured by using the LIF system built in our laboratory and exhibited different FLCs with different nitrogen fertilization levels. The correlation between fluorescence intensity ratios (F685/F460, F735/F460 and F735/F685) and the dose of N fertilization was established and analysed. Then, the BPNN algorithm was utilized to validate that the different N fertilization levels can be classified based on the three FLCs. The overall identification accuracies of 2014 and 2015 were 90% and 92.5%, respectively. Experimental results demonstrated that the three FLCs with the help of multivariate analysis can be served as a helpful tool in the evaluation of paddy rice N fertilization levels. Besides, this study can also provide guidance for the selection of LIF Lidar channels in the following research.

Keywords: fluorescence characteristics; remote sensing; nutrient stress; Oryza sativa; machine learning

Published: September 30, 2017  Show citation

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YANG J, Lin D, GONG W, SUN J, SHI S, CHEN B. Application of the chlorophyll fluorescence ratio in evaluation of paddy rice nitrogen status. Plant Soil Environ. 2017;63(9):396-401. doi: 10.17221/460/2017-PSE.
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    References

    1. Chappelle E.W., McMurtrey J.E., Wood F.M., Newcomb W.W. (1984a): Laser-induced fluorescence of green plants. 2: LIF caused by nutrient deficiencies in corn. Applied Optics, 23: 139-142. Go to original source... Go to PubMed...
    2. Chappelle E.W., Wood F.M., McMurtrey J.E., Newcomb W.W. (1984b): Laser-induced fluorescence of green plants. 1: A technique for the remote detection of plant stress and species differentiation. Applied Optics, 23: 134-138. Go to original source... Go to PubMed...
    3. Chappelle E.W., Wood F.M.Jr., Newcomb W.W., McMurtrey J.E. (1985): Laser-induced fluorescence of green plants. 3: LIF spectral signatures of five major plant types. Applied Optics, 24: 74-80. Go to original source... Go to PubMed...
    4. Gong W., Song S.L., Zhu B., Shi S., Li F.Q., Cheng X.W. (2012): Multi-wavelength canopy LiDAR for remote sensing of vegetation: Design and system performance. ISPRS Journal of Photogrammetry and Remote Sensing, 69: 1-9. Go to original source...
    5. Hák R., Lichtenthaler H.K., Rinderle U. (1990): Decrease of the chlorophyll fluorescence ratio F690/F730 during greening and development of leaves. Radiation and Environmental Biophysics, 29: 329-336. Go to original source... Go to PubMed...
    6. Hoge F.E., Swift R.N. (1981): Airborne simultaneous spectroscopic detection of laser-induced water Raman backscatter and fluorescence from chlorophyll a and other naturally occurring pigments. Applied Optics, 20: 3197-3205. Go to original source... Go to PubMed...
    7. Huang X.H., Kurata N., Wei X.H., Wang Z.-X., Wang A., Zhao Q., Zhao Y., Liu K.Y., Lu H.Y., Li W.J., Guo Y.L., Lu Y.Q., Zhou C.C., Fan D.L., Weng Q.J., Zhu C.R., Huang T., Zhang L., Wang Y.C., Feng L., Furuumi H., Kubo T., Miyabayashi T., Yuan X.P., Xu Q., Dong G.J., Zhan Q.L., Li C.Y., Fujiyama A., Toyoda A., Lu T.T., Feng Q., Qian Q., Li J.Y., Han B. (2012): A map of rice genome variation reveals the origin of cultivated rice. Nature, 490: 497-501. Go to original source... Go to PubMed...
    8. Li W., Sun G., Niu Z., Gao S., Qiao H.L. (2014): Estimation of leaf biochemical content using a novel hyperspectral full-waveform LiDAR system. Remote Sensing Letters, 5: 693-702. Go to original source...
    9. Lichtenthaler H.K., Buschmann C. (1987): Chlorophyll fluorescence spectra of green bean leaves. Journal of Plant Physiology, 129: 137-147. Go to original source...
    10. McMurtrey J.E., Chappelle E.W., Kim M.S., Meisinger J.J., Corp L.A. (1994): Distinguishing nitrogen fertilization levels in field corn (Zea mays L.) with actively induced fluorescence and passive reflectance measurements. Remote Sensing of Environment, 47: 36-44. Go to original source...
    11. Pedrós R., Moya I., Goulas Y., Jacquemoud S. (2008): Chlorophyll fluorescence emission spectrum inside a leaf. Photochemical and Photobiological Sciences, 7: 498-502. Go to original source... Go to PubMed...
    12. Schweiger J., Lang M., Lichtenthaler H.K. (1996): Differences in fluorescence excitation spectra of leaves between stressed and non-stressed plants. Journal of Plant Physiology, 148: 536-547. Go to original source...
    13. Subhash N., Mohanan C.N. (1994): Laser-induced red chlorophyll fluorescence signatures as nutrient stress indicator in rice plants. Remote Sensing of Environment, 47: 45-50. Go to original source...
    14. Tian Y.-C., Gu K.-J., Chu X., Yao X., Cao W.-X., Zhu Y. (2014): Comparison of different hyperspectral vegetation indices for canopy leaf nitrogen concentration estimation in rice. Plant and Soil, 376: 193-209. Go to original source...
    15. Wu C.Y., Niu Z., Tang Q., Huang W.J. (2008): Estimating chlorophyll content from hyperspectral vegetation indices: Modeling and validation. Agricultural and Forest Meteorology, 148: 1230-1241. Go to original source...
    16. Yang J., Shi S., Gong W., Du L., Ma Y.Y., Zhu B., Song S.L. (2015): Application of fluorescence spectrum to precisely inverse paddy rice nitrogen content. Plant, Soil and Environment, 61: 182-188. Go to original source...
    17. Yang J., Du L., Sun J., Zhang Z., Chen B., Shi S., Gong W., Song S. (2016): Estimating the leaf nitrogen content of paddy rice by using the combined reflectance and laser-induced fluorescence spectra. Optics Express, 24: 19354-19365. Go to original source... Go to PubMed...
    18. Yang J., Sun J., Du L., Chen B., Zhang Z., Shi S., Gong W. (2017): Effect of fluorescence characteristics and different algorithms on the estimation of leaf nitrogen content based on laserinduced fluorescence lidar in paddy rice. Optics Express, 25: 3743-3755. Go to original source... Go to PubMed...
    19. Yao X., Zhu Y., Tian Y.C., Feng W., Cao W.X. (2010): Exploring hyperspectral bands and estimation indices for leaf nitrogen accumulation in wheat. International Journal of Applied Earth Observation and Geoinformation, 12: 89-100. Go to original source...
    20. Živčák M., Brestič M., Olšovská K. (2008): Assessment of physiological parameters useful in screening for tolerance to soil drought in winter wheat (Triticum aestivum L.) genotypes. Cereal Research Communications, 36: 1943-1946.

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