4.7 Article

Evaluation of the performance of portable visible-infrared instruments for the prediction of soil properties

期刊

BIOSYSTEMS ENGINEERING
卷 161, 期 -, 页码 24-36

出版社

ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.biosystemseng.2017.06.017

关键词

Mid-infrared; Near-infrared; Partial least squares regression; Portable; Soil; Spectroscopy

资金

  1. Grains Research and Development Corporation (GRDC project Soil infrared capability) [CS000045]

向作者/读者索取更多资源

Good soil management requires large amounts of soil data which are expensive to provide using traditional laboratory methods. Soil infrared spectroscopy including portable/miniaturized visible-infrared spectrometers offers a cost-effective solution. There is a need to test and compare the performance of portable/miniaturized mid-infrared (MIR) and visible near-infrared (vis-NIR) spectrometers for the prediction of soil properties across a range of soils. For this assessment, 458 soil samples from Australia were scanned by four vis-NIR and MIR portable/miniature spectrometers and partial least squares regressions (PLSR) applied for the prediction of 17 properties in soils dried at 40 degrees C and sieved to <2 mm. The performance of these instruments was tested and compared to a reference benchtop MIR/NIR instrument. Mid-infrared handheld instruments provided the best performance, the vis-NIR instrument the next most successful, and the miniature NIR instrument with a restricted spectral range (950-1650 nm) being less successful. When models using the same spectral range obtained by different instruments were compared, similar performance was achieved, thus the spectral quality provided by different instrumentation was not decisive in determining prediction accuracy. Many new portable infrared instruments have restricted spectral ranges, thus a number of different spectral ranges in both the MIR and vis-NIR were assessed to determine the optimal range for prediction of soil properties. It was concluded that the range 1650-5000 nm would be ideal. Crown Copyright (C) 2017 Published by Elsevier Ltd on behalf of IAgrE. All rights reserved.

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