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Application of the chlorophyll fluorescence ratio in evaluation of paddy rice nitrogen status

Author

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  • Jian YANG

    (Faculty of Information Engineering, China University of Geosciences, Wuhan, Hubei, P.R. China
    State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, Hubei, P.R. China)

  • Lin DU

    (Faculty of Information Engineering, China University of Geosciences, Wuhan, Hubei, P.R. China)

  • Wei GONG

    (State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, Hubei, P.R. China
    Collaborative Innovation Centre of Geospatial Technology, Wuhan, Hubei, P.R. China)

  • Jia SUN

    (State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, Hubei, P.R. China)

  • Shuo SHI

    (State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, Hubei, P.R. China
    Collaborative Innovation Centre of Geospatial Technology, Wuhan, Hubei, P.R. China)

  • Biwu CHEN

    (State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, Hubei, P.R. China)

Abstract

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.

Suggested Citation

  • Jian YANG & Lin DU & Wei GONG & Jia SUN & Shuo SHI & Biwu CHEN, 2017. "Application of the chlorophyll fluorescence ratio in evaluation of paddy rice nitrogen status," Plant, Soil and Environment, Czech Academy of Agricultural Sciences, vol. 63(9), pages 396-401.
    • Handle: RePEc:caa:jnlpse:v:63:y:2017:i:9:id:460-2017-pse
    DOI: 10.17221/460/2017-PSE
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    References listed on IDEAS

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    1. J. Yang & S. Shi & W. Gong & L. Du & Y.Y. Ma & B. Zhu & S.L. Song, 2015. "Application of fluorescence spectrum to precisely inverse paddy rice nitrogen content," Plant, Soil and Environment, Czech Academy of Agricultural Sciences, vol. 61(4), pages 182-188.
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    1. J. Yang & W. Gong & S. Shi & L. Du & J. Sun & Y.-Y. Ma & S.-L. Song, 2015. "Accurate identification of nitrogen fertilizer application of paddy rice using laser-induced fluorescence combined with support vector machine," Plant, Soil and Environment, Czech Academy of Agricultural Sciences, vol. 61(11), pages 501-506.

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