TITLE:
Monitoring Soil Nitrate Nitrogen Based on Hyperspectral Data in the Apple Orchards
AUTHORS:
Yu Wei, Xicun Zhu, Cheng Li, Lizhen Cheng, Ling Wang, Gengxing Zhao, Yuanmao Jiang
KEYWORDS:
Hyperspectrum, Nitrate Nitrogen Content, Support Vector Machine, Sensitive Wavelength
JOURNAL NAME:
Agricultural Sciences,
Vol.8 No.1,
January
5,
2017
ABSTRACT: This paper is aimed to monitor the soil nitrate nitrogen content in the apple orchards rapidly, accurately and in real time by making full use of the effective information of soil spectra. The 96 air-dried soil samples of the apple orchards in Qixia county, Yantai city, Shandong province were used as the data source. Spectral measurements of soil samples were carried out by ASD Fieldspec 3 in the darkroom, and the content of the soil nitrate nitrogen was determined by chemical method. Then the hyperspectral reflectance of soil samples were preprocessed by Multivariate Scatter Correction (MSC) and First Derivative (FD), the correlation analysis was carried out with the soil nitrate nitrogen content. The sensitive wavelength of soil nitrate nitrogen was screened. Finally, the Support Vector Machine (SVM) model for the soil nitrate nitrogen content was established. The results showed that the selected sensitive wavelength were 617 nm, 760 nm, 1239 nm, 1442 nm, 1535 nm, 1695 nm, 1776 nm, 1907 nm and 2088 nm. Hyperspectral monitoring model was established by SVM, in which the prediction set R2 was 0.959, RMSE was 0.281, RPD was 3.835; the correction set R2 was 0.822, RMSE was 0.392, RPD was 2.037. The SVM model could be used to monitor the soil nitrate content accurately.