In search of an optimum sampling algorithm for prediction of soil properties from infrared spectra.

ABSTRACT:

Background

The use of visible-near infrared (vis-NIR) spectroscopy for rapid soil characterisation has gained a lot of interest in recent times. Soil spectra absorbance from the visible-infrared range can be calibrated using regression models to predict a set of soil properties. The accuracy of these regression models relies heavily on the calibration set. The optimum sample size and the overall sample representativeness of the dataset could further improve the model performance. However, there is no guideline on which sampling method should be used under different size of datasets.

Methods

Here, we show different sampling algorithms performed differently under different data size and different regression models (Cubist regression tree and Partial Least Square Regression (PL

SUBMITTER: Ng W 

PROVIDER: S-EPMC6173947 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature