Diagnosis of Phosphorus Nutrition in Winter Wheat Based on First Derivative Spectra and Radial Basis Function Neural Network

作者：Liu Wei; Chang Qing-rui^*; Guo Man; Xing Dong-xing; Yuan Yong-sheng

来源：Spectroscopy and Spectral Analysis, 2011, 31(4): 1092-1096.

DOI：10.3964/j.issn.1000-0593(2011)04-1092-05

• Visible/near infrared spectra
• Winter wheat
• Phosphorus nutrition
• Wavelet denoising
• Numerical integration
• Radial basis function neural network

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 similar to 435 and 669 similar to 680 nm. (2) Sensitive wavebands in first derivative of spectra are 481 similar to 493 and 685 similar to 696 nm. (3) Trained RBFNN can learn and seize the linearity/non-linearity mapping between samples and output targets.

• 出版日期2011-4
• 单位咸阳师范学院; 西北农林科技大学