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

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. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

Soil spectroscopy, especially in the infrared range, has emerged as a powerful technique over the past two decades, offering rapid, cost-effective, quantitative, and eco-friendly analysis compared to traditional chemical methods. By combining infrared techniques with chemometrics and machine learning tools, it can provide accurate predictions of soil properties and outperform traditional methods in fertilizer recommendations. Challenges and opportunities of soil spectroscopy as an efficient diagnostic tool in soil science were also discussed.