Soil spectroscopy with the use of chemometrics, machine learning and pre-processing techniques in soil diagnosis: Recent advances–A review

A - Papers appearing in refereed journals

Barra, I., Haefele, S. M., Sakrabani, R. and Kebede, F. 2021. Soil spectroscopy with the use of chemometrics, machine learning and pre-processing techniques in soil diagnosis: Recent advances–A review. Trends in Analytical Chemistry - TrAC. 135 (February), p. 116166. https://doi.org/10.1016/j.trac.2020.116166

AuthorsBarra, I., Haefele, S. M., Sakrabani, R. and Kebede, F.
Abstract

Over the past two decades soil spectroscopy, particularly, in the infrared range, is becoming a powerful technique to simplify analysis relative to the traditional chemical methods. It is known as a rapid, cost-effective, quantitative and eco-friendly technique, which can provide hyperspectral data with narrow and numerous wavebands, both in the laboratory and in the field. In this context, the present article reviews the recent developments in mid and near infrared techniques coupled with chemometrics and machine learning tools in addition to the preprocessing transformations and variable selection strategies to diagnose soil physical and chemical properties. Both spectral techniques demonstrated a good ability to provide accurate predictions of specific properties. Moreover, the MIR spectroscopy outperformed NIR for the estimation of most indicators used for fertilizers recommendation. Herein, a detailed overview on the opportunities and challenges that soil spectroscopy offers as efficient diagnostic tool in soil science was provided.

KeywordsSoil diagnosis; Infrared spectroscopy; Chemometrics; Machine learning; Data preprocessing
Year of Publication2021
JournalTrends in Analytical Chemistry - TrAC
Journal citation135 (February), p. 116166
Digital Object Identifier (DOI)https://doi.org/10.1016/j.trac.2020.116166
Open accessPublished as non-open access
FunderBiotechnology and Biological Sciences Research Council
Publication dates
OnlineFeb 2021
PublisherElsevier

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