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

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-propa...

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Main Authors: J. Yang, W. Gong, S. Shi, L. Du, J. Sun, S.-L. Song
Format: Article
Language:English
Published: Czech Academy of Agricultural Sciences 2016-04-01
Series:Plant, Soil and Environment
Subjects:
Online Access:https://pse.agriculturejournals.cz/artkey/pse-201604-0005_estimation-of-nitrogen-content-based-on-fluorescence-spectrum-and-principal-component-analysis-in-paddy-rice.php
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author J. Yang
W. Gong
S. Shi
L. Du
J. Sun
S.-L. Song
author_facet J. Yang
W. Gong
S. Shi
L. Du
J. Sun
S.-L. Song
author_sort J. Yang
collection DOAJ
description 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.
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spelling doaj.art-29bb1e5f4ac14c0689201288843c02442023-02-23T03:46:23ZengCzech Academy of Agricultural SciencesPlant, Soil and Environment1214-11781805-93682016-04-0162417818310.17221/802/2015-PSEpse-201604-0005Estimation of nitrogen content based on fluorescence spectrum and principal component analysis in paddy riceJ. Yang0W. Gong1S. Shi2L. Du3J. Sun4S.-L. Song5State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, P.R. ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, P.R. ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, P.R. ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, P.R. ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, P.R. ChinaWuhan 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.https://pse.agriculturejournals.cz/artkey/pse-201604-0005_estimation-of-nitrogen-content-based-on-fluorescence-spectrum-and-principal-component-analysis-in-paddy-rice.phpremote sensingoryza sativamacroelementenvironmental pollutionleaf nitrogen content
spellingShingle J. Yang
W. Gong
S. Shi
L. Du
J. Sun
S.-L. Song
Estimation of nitrogen content based on fluorescence spectrum and principal component analysis in paddy rice
Plant, Soil and Environment
remote sensing
oryza sativa
macroelement
environmental pollution
leaf nitrogen content
title Estimation of nitrogen content based on fluorescence spectrum and principal component analysis in paddy rice
title_full Estimation of nitrogen content based on fluorescence spectrum and principal component analysis in paddy rice
title_fullStr Estimation of nitrogen content based on fluorescence spectrum and principal component analysis in paddy rice
title_full_unstemmed Estimation of nitrogen content based on fluorescence spectrum and principal component analysis in paddy rice
title_short Estimation of nitrogen content based on fluorescence spectrum and principal component analysis in paddy rice
title_sort estimation of nitrogen content based on fluorescence spectrum and principal component analysis in paddy rice
topic remote sensing
oryza sativa
macroelement
environmental pollution
leaf nitrogen content
url https://pse.agriculturejournals.cz/artkey/pse-201604-0005_estimation-of-nitrogen-content-based-on-fluorescence-spectrum-and-principal-component-analysis-in-paddy-rice.php
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