Accurate identification of nitrogen fertilizer application of paddy rice using laser-induced fluorescence combined with support vector machine

J. Yang, W. Gong, S. Shi, L. Du, J. Sun, Y.-Y. Ma and S.-L. Song
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J. Yang: StateKey Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, P.R. China
W. Gong: StateKey Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, P.R. China
S. Shi: StateKey Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, P.R. China
L. Du: StateKey Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, P.R. China
J. Sun: StateKey Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, P.R. China
Y.-Y. Ma: StateKey Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, P.R. China
S.-L. Song: Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan, P.R. China

Plant, Soil and Environment, 2015, vol. 61, issue 11, 501-506

Abstract: To identify accurately the doses of nitrogen (N) fertilizer and improve the photosynthetic efficiency of paddy rice, laser induced fluorescence (LIF) technique combined with the support vector machine (SVM) and principal component analysis (PCA) is proposed in this paper. The LIF technology, in which the ultraviolet light (355 nm) is applied as an excitation light source, is employed to measure fluorescence spectra of paddy rice. These fluorescence spectra demonstrate that the fluorescence spectral characteristics of paddy rice leaves with different doses of N fertilizer have distinct differences from each other. Then, PCA and SVM are implemented to extract the features of fluorescence spectra and to recognize different doses of N fertilizer, respectively. The overall recognition accuracy can reach 95%. The results show that the LIF technology combined with PCA and SVM is a convenient, rapid, and sensitive diagnostic method for detecting N levels of paddy rice. Thus, it will also be convenient for farmers to manage accurately their fertilization strategies.

Keywords: remote sensing; macronutrient; multivariate analysis; chlorophyll content; Oryza sativa (search for similar items in EconPapers)
Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:caa:jnlpse:v:61:y:2015:i:11:id:496-2015-pse

DOI: 10.17221/496/2015-PSE

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