Plant Soil Environ., 2016, 62(4):178-183 | DOI: 10.17221/802/2015-PSE

Estimation of nitrogen content based on fluorescence spectrum and principal component analysis in paddy riceOriginal Paper

J. Yang1, W. Gong1,2, S. Shi1,2,3, L. Du1,4, J. Sun1, S.-L. Song5
1 State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, P.R. China
2 Collaborative Innovation Center of Geospatial Technology, Wuhan, P.R. China
3 School of Resource and Environmental Sciences, Wuhan University, Wuhan, P.R. China
4 School of Physics and Technology, Wuhan University, Wuhan, P.R. China
5 Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan,

Paddy rice is one of the most important cereal crops in China. Nitrogen (N) is closely related to crops production by influencing the photosynthetic efficiency of paddy rice. In this study, laser-induced fluorescence (LIF) technology with the help of principal component analysis (PCA) and back-propagation neural network (BPNN) is proposed to monitor leaf N content (LNC) of paddy rice. The PCA is utilized to extract the characteristic variables of LIF spectra by analysing the major attributes. The results showed that the first three principal components (PCs) can explain 95.76% and 93.53% of the total variance contained in the fluorescence spectra for tillering stage and shooting stage, respectively. Then, BPNN was utilized to inverse the LNC on the basis of the first three PCs as input variables and can obtain the satisfactory inversion results (R2 of tillering stage and shooting stage are 0.952 and 0.931, respectively; residual main range from -0.2 to 0.2 mg/g). The experimental results demonstrated that LIF technique combined with multivariate analysis will be a useful method for monitoring the LNC of paddy rice, which can provide consultations for the decision-making of peasants in their N fertilization strategies.

Keywords: remote sensing; Oryza sativa; macroelement; environmental pollution; leaf nitrogen content

Published: April 30, 2016  Show citation

ACS AIP APA ASA Harvard Chicago IEEE ISO690 MLA NLM Turabian Vancouver
Yang J, Gong W, Shi S, Du L, Sun J, Song S-L. Estimation of nitrogen content based on fluorescence spectrum and principal component analysis in paddy rice. Plant Soil Environ.. 2016;62(4):178-183. doi: 10.17221/802/2015-PSE.
Share...
Download citation
  • BibTeX (.bib)
  • Bookends (.ris)
  • EasyBib (.ris)
  • EndNote (.enw)
  • EndNote 8 (.xml)
  • ISI WoS (.isi)
  • Medlars (.medlars)
  • Mendeley (.ris)
  • MODS (.xml)
  • Papers (.ris)
  • RefWorks (.txt)
  • RefManager (.ris)
  • RIS (.ris)
  • MS Word (.xml)
  • Zotero (.ris)
    • Open full article

    References

    1. Apostol S., Viau A.A., Tremblay N. (2007): A comparison of multiwavelength laser-induced fluorescence parameters for the remote sensing of nitrogen stress in field-cultivated corn. Canadian Journal of Remote Sensing, 33: 150-161. Go to original source...
    2. Aleksandrov V., Krasteva V., Paunov M., Chepisheva M., Kousmanova M., Kalaji H.M., Goltsev V. (2014): Deficiency of some nutrient elements in bean and maize plants analyzed by luminescent method. Bulgarian Journal of Agricultural Science, 20: 24-30.
    3. Chappelle E.W., Wood F.M., McMurtrey J.E., Newcomb W.W. (1984): 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...
    4. Chappelle E.W., McMurtrey J.E., Kim M.S. (1991): Identification of the pigment responsible for the blue fluorescence band in the laser induced fluorescence (LIF) spectra of green plants, and the potential use of this band in remotely estimating rates of photosynthesis. Remote Sensing of Environment, 36: 213-218. Go to original source...
    5. Cao Q., Miao Y.X., Wang H.Y., Huang S., Cheng S.S., Khosla R., Jiang R.F. (2013): Non-destructive estimation of rice plant nitrogen status with crop circle multispectral active canopy sensor. Field Crops Research, 154: 133-144. Go to original source...
    6. Feng W., Yao X., Zhu Y., Tian Y.C., Cao W.X. (2008): Monitoring leaf nitrogen status with hyperspectral reflectance in wheat. European Journal of Agronomy, 28: 394-404. Go to original source...
    7. Gong W., Song S.L., Zhu B., Shi S., Li F., 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...
    8. Kalaji H.M., Oukarroum A., Alexandrov V., Kouzmanova M., Brestic M., Zivcak M., Samborska I.A., Cetner M.D., Allakhverdiev S.I., Goltsev V. (2014): Identification of nutrient deficiency in maize and tomato plants by in vivo chlorophyll a fluorescence measurements. Plant Physiology and Biochemistry, 81: 16-25. Go to original source... Go to PubMed...
    9. Malenovský Z., Mishra K.B., Zemek F., Rascher U., Nedbal L. (2009): Scientific and technical challenges in remote sensing of plant canopy reflectance and fluorescence. Journal of Experimental Botany, 60: 2987-3004. Go to original source... Go to PubMed...
    10. Meroni M., Rossini M., Guanter L., Alonso L., Rascher U., Colombo R., Moreno J. (2009): Remote sensing of solar-induced chlorophyll fluorescence: Review of methods and applications. Remote Sensing of Environment, 113: 2037-2051. Go to original source...
    11. Olszewski J., Makowska M., Pszczółkowska A., Okorski A., Bieniaszewski T. (2014): The effect of nitrogen fertilization on flag leaf and ear photosynthesis and grain yield of spring wheat. Plant, Soil and Environment, 60: 531-536. Go to original source...
    12. Samborska I.A., Alexandrov V., Sieczko L., Kornatowska B., Goltsev V., Cetner M.D., Kalaji H.M. (2014): Artificial neural networks and their application in biological and agricultural research. Signpost Open Access Journal of NanoPhotoBioSciences, 2: 14-30.
    13. Tuba Z., Saxena D.K., Srivastava K., Singh S., Czobel S., Kalaji H.M. (2010): Chlorophyll a fluorescence measurements for validating the tolerant bryophytes for heavy metal (Pb) biomapping. Current Science (Bangalore), 98: 1505-1508.
    14. Tremblay N., Wang Z., Cerovic Z.G. (2011): Sensing crop nitrogen status with fluorescence indicators: A review. Agronomy for Sustainable Development, 32: 451-464. Go to original source...
    15. Yi Q.X., Huang J.F., Wang F.M., Wang X.Z., Liu Z.Y. (2007): Monitoring rice nitrogen status using hyperspectral reflectance and artificial neural network. Environmental Science and Technology, 41: 6770-6775. Go to original source... Go to PubMed...
    16. Yang J., Gong W., Shi S., Du L., Sun J., Ma Y.-Y., Song S.L. (2015a): Accurate identification of nitrogen fertilizer application of paddy rice using laser-induced fluorescence combined with support vector machine. Plant, Soil and Environment, 61: 501-506. Go to original source...
    17. Yang J., Shi S., Gong W., Du L., Ma Y.Y., Zhu B., Song S.L. (2015b): Application of fluorescence spectrum to precisely inverse paddy rice nitrogen content. Plant, Soil and Environment, 61: 182-188. Go to original source...
    18. Zong R.W., Zhi Y., Yao B., Gao J.X., Stec A.A. (2014): Classification and identification of soot source with principal component analysis and back-propagation neural network. Australian Journal of Forensic Sciences, 46: 224-233. Go to original source...
    19. Živčák M., Olšovská K., Slamka P., Galambošová J., Rataj V., Shao H.-B., Brestič M. (2014a): Measurements of chlorophyll fluorescence in different leaf positions may detect nitrogen deficiency in wheat. Zemdirbyste-Agriculture, 101: 437-444. Go to original source...
    20. Živčák M., Olšovská K., Slamka P., Galambošová J., Rataj V., Shao H.B., Brestič M. (2014b): Application of chlorophyll fluorescence performance indices to assess the wheat photosynthetic functions influenced by nitrogen deficiency. Plant, Soil and Environment, 60: 210-215. Go to original source...

    This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY NC 4.0), which permits non-comercial use, distribution, and reproduction in any medium, provided the original publication is properly cited. No use, distribution or reproduction is permitted which does not comply with these terms.


    Return to the content