Document Detail

Monitoring rice nitrogen status using hyperspectral reflectance and artificial neural network.
MedLine Citation:
PMID:  17969693     Owner:  NLM     Status:  MEDLINE    
Over use of nitrogen fertilization can result in groundwater pollution. Tools that can rapidly quantify the nitrogen status are needed for efficient fertilizer management and would be very helpful in reducing the environmental pollution caused by excessive nitrogen application. Remote sensing has a proven ability to provide spatial and temporal measurements of surface properties. In this study, the MLR (multiple linear regression) and ANN (artificial neural network) modeling methods were applied to the monitoring of rice N (nitrogen concentration, mg nitrogen g(-1) leaf dry weight) status using leaf level hyperspectral reflectance with two different input variables, and as a result four estimation models were proposed. RMSE (root-mean-square error), REP (relative error of prediction), R2 (coefficient of determination), as well as the intercept and slope between the observed and predicted N were used to test the performance of models. Very good agreements between the observed and the predicted N were obtained with all proposed models, which was especially true for the R-ANN (artificial neural network based on reflectance selected using MLR) model. Compared to the other three models, the R-ANN model improved the results by lowering the RMSE by 14.2%, 32.1%, and 31.5% for the R-LR (linear regression based on reflectance) model, PC-LR (linear regression based on principal components scores) model, and PC-ANN (artificial neural network based on principal components scores) model, respectively. It was concluded that the ANN algorithm may provide a useful exploratory and predictive tool when applied on hyperspectral reflectance data for nitrogen status monitoring. Besides, although the performance of MLR was superior to PCA used for ANN inputs selection, the encouraging results of PC-based models indicated the promising potential of ANN combined with PCA application on hyperspectral reflectance analysis.
Qiu-Xiang Yi; Jing-Feng Huang; Fu-Min Wang; Xiu-Zhen Wang; Zhan-Yu Liu
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  Environmental science & technology     Volume:  41     ISSN:  0013-936X     ISO Abbreviation:  Environ. Sci. Technol.     Publication Date:  2007 Oct 
Date Detail:
Created Date:  2007-10-30     Completed Date:  2008-01-24     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  0213155     Medline TA:  Environ Sci Technol     Country:  United States    
Other Details:
Languages:  eng     Pagination:  6770-5     Citation Subset:  IM    
Institute of Agricultural Remote Sensing & Information Application, Huajiachi Campus, Zhejiang University, Hangzhou 310029, China.
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MeSH Terms
Environmental Monitoring
Neural Networks (Computer)*
Nitrogen / analysis*
Oryza sativa / chemistry*
Plant Leaves / chemistry
Regression Analysis
Reg. No./Substance:

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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