| 光谱学与光谱分析 |
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| Diagnosis of Phosphorus Nutrition in Winter Wheat Based on First Derivative Spectra and Radial Basis Function Neural Network |
| LIU Wei1, CHANG Qing-rui1*, GUO Man1, XING Dong-xing1, 2, YUAN Yong-sheng1 |
1. College of Resources and Environment, Northwest A&F University, Yangling 712100, China
2. Department of Resources and Environment, Xianyang Normal College, Xianyang 712000, China |
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Abstract The hyperspectral leaf reflectance in winter wheat was measured under 4 phosphorus levels at different growth stages, i.e. revival stage, jointing stage, tassel stage and grouting stage. And their first derivative of spectra were calculated and denoised by the threshold denoising method based on wavelet transform. After studying characteristics of the two kinds of spectra resulting from different phosphorus contents levels as well as correlations between leaf phosphorus contents and spectral values, sensitive wavebands and four kinds of absorption areas were extracted. Then the four kinds of absorption areas and their corresponding leaf phosphorus content were normalized and input to RBFNN. Results show that: (1) Sensitive wavebands for monitoring leaf phosphorus contents in original leaf spectra are 426~435 and 669~680 nm. (2) Sensitive wavebands in first derivative of spectra are 481~493 and 685~696 nm. (3) Trained RBFNN can learn and seize the linearity/non-linearity mapping between samples and output targets.
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Received: 2010-05-10
Accepted: 2010-08-20
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Corresponding Authors:
CHANG Qing-rui
E-mail: chqr@nwsuaf.edu.cn
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