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

• DOI: 10.1016/j.trac.2020.116166
• Publication Date: 2020-12-30T02:47:32Z
• Authors: Issam Barra; Stephan M. Haefele; Ruben Sakrabani; Fassil Kebede
• Keywords: Soil diagnosis; 670; Machine learning; 0401 agriculture, forestry, and fisheries; Data preprocessing; 04 agricultural and veterinary sciences; Chemometrics; Infrared spectroscopy; 01 natural sciences; 0105 earth and related environmental sciences

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.