Quantification of Nitrogen Status in Rice by Least Squares Support Vector Machines and Reflectance Spectroscopy |
| |
Authors: | Yongni Shao Chunjiang Zhao Yidan Bao Yong He |
| |
Affiliation: | (1) College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310029, China;(2) National Engineering Research Center for Information Technology in Agriculture, Beijing, China; |
| |
Abstract: | The estimation of nitrogen status non-destructively in rice was performed using canopy spectral reflectance with visible and
near-infrared reflectance (Vis/NIR) spectroscopy. The canopy spectral reflectance of rice grown with different levels of nitrogen
inputs was determined at several important growth stages. This study was conducted at the experiment farm of Zhejiang University,
Hangzhou, China. The soil plant analysis development (SPAD) value was used as a reference data that indirectly reflects nitrogen
status in rice. A total of 64 rice samples were used for Vis/NIR spectroscopy at 325–1075 nm using a field spectroradiometer,
and chemometrics of partial least square (PLS) was used for regression. The correlation coefficient (r), root mean square error of prediction, and bias in prediction set by PLS were, respectively, 0.8545, 0.7628, and 0.0521
for SPAD value prediction in tillering stage, 0.9082, 0.4452, and −0.0109 in booting stage, and 0.8632, 0.7469, and 0.0324
in heading stage. Least squares support vector machine (LS-SVM) model was compared with PLS and back propagation neural network
methods. The results showed that LS-SVM was superior to the conventional linear and non-linear methods in predicting SPAD
values of rice. Independent component analysis was executed to select several sensitive wavelengths (SWs) based on loading
weights; the optimal LS-SVM model was achieved with SWs of 560, 575–580, 700, 730, and 740 nm for SPAD value prediction in
booting stage. It is concluded that Vis/NIR spectroscopy combined with LS-SVM regression method is a promising technique to
monitor nitrogen status in rice. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|