科研之友
微信
新浪微博
Facebook
分享链接
摘要
A rapid and reliable method is required to determine the N status of pear (Pyrus communis L.) leaves during the growing season for timely fertilization to improve the yields and fruit quality. In the present study, we evaluated visible and near-infrared reflectance (Vis/NIR) spectra of fresh pear leaves using partial least squares (PLS) regression to determine the N concentration of fresh pear leaves. In addition, we studied the performance of modified spectra generated using different preprocessing techniques. A total of 450 leaf samples were collected from 6-yr-old pear trees of two cultivars, and randomly separated into two subsets (calibration subset [294 samples] and validation subset [180 samples]) after excluding outliers by using principle component analysis. Results showed that the model built using full spectra performed better than that developed using characteristic wavelength segments. In addition, we found that original spectral proved to provide better accuracy than derivative spectra. Among the studied preprocessing techniques, moving average smoothing (MAS) technique improved accuracy the most. Overall results suggested that PLS regression with preprocessing of full spectra using MAS is optimal method for modeling N concentration of fresh pear leaves which yielded 0.961 and 0.953 coefficient of determination (R-2) for calibration and cross-validation, respectively. The validation of this method resulted high R-2 value (0.847) and low mean relative error (4.48%). In conclusion, this model could provide a rapid and more reliable method to determine the total N concentration in fresh pear leaves and could be useful for fertilization management in pear orchards.
- 出版日期2014-10
- 单位南京农业大学
