Paper Title:
Selection of Spectral Preprocessing Methods for Soil Texture Classification
  Abstract

Fast classification of soil with different texture is essential for site-specific application of different inputs into farmland. Total 178 soil samples with five textures were collected from Silsoe Farm, Cranfield University, England. Using a Vis/NIR spectrophotometer (LabSpec2500, ASD), spectra of soil samples were scanned for the study. Amongst various pre-processing methods, smoothing with moving average(MA), standard normal variation(SNV) and 1st derivatives were mainly investigated. PCA was applied to evaluate the discriminative capacity of the pre-processing methods on soil spectra. The score plot of PCs shows that 1st derivative with variable smoothing points can help classify soil samples much more effectively than others. With the increase of smoothing points, the cumulative variance of first few PCs in PCA tends to increase while the discriminative capability based on these PCs becomes worse. A trade-off between cumulative variance and discriminative capability should be concerned. In the study, an appropriate range of smoothing points in the 1st derivative is 7-21.

  Info
Periodical
Advanced Materials Research (Volumes 181-182)
Edited by
Qi Luo and Yuanzhi Wang
Pages
416-421
DOI
10.4028/www.scientific.net/AMR.181-182.416
Citation
H. Q. Yang, B. Y. Kuang, A. M. Mouazen, "Selection of Spectral Preprocessing Methods for Soil Texture Classification", Advanced Materials Research, Vols. 181-182, pp. 416-421, 2011
Online since
January 2011
Export
Price
$35.00
Share

In order to see related information, you need to Login.

In order to see related information, you need to Login.

Authors: Peng Cheng Nie, Weiong Zhang, Yan Yang, Di Wu, Yong He
Abstract:Visible/near-infrared spectroscopy (NIRS) is the millimeter wave ,It is the high speed and non-destructiveness method, high precision and...
159
Authors: Qiang Shao, Chang Jian Feng
Chapter 2: Innovative Design Methodology
Abstract:To distinguish chatter gestation, chatter recognition method based on hybrid PCA(Principal Compenent Analysis) and SVM(Support Vector...
190
Authors: Pai Li, Yao Xiang Li
Chapter 3: Material Engineering
Abstract:In this paper, an integration of BP neural network and PCA for modeling wood water content of larch combined with NIRS was investigated. The...
253
Authors: Elena A. Kochegurova, Ekaterina S. Gorokhova
Chapter 7: Information Technology, Data and Signal Processing
Abstract:A method of modeling derivatives in a real-time non-stationary process is proposed. This concept is based on approximation of variation...
920