Soil Spectroscopy: When Spectroscopy and Machine Learning Combine to Solve Agricultural Soil Diagnostic Problems

Soil spectroscopy that combines spectroscopic analysis and machine learning tools is an important tool for analyzing soil properties and has gained significant attention in recent years due to its potential to provide rapid and non-destructive assessment of soil properties. This book chapter provides an overview of the applications of soil spectroscopy in agriculture and environmental management. It describes the different types of spectroscopic techniques used for soil analysis, including Vis–NIR, MIR, Raman spectroscopy and LIBS. The chapter discusses the challenges associated with soil spectroscopy, such as the need for accurate and representative soil sampling, and the need for calibration models to be developed and validated using robust statistical methods. It also highlights the potential of machine learning algorithms for the development of predictive models. This chapter provides case studies on the use of soil spectroscopy for assessing soil health, nutrient status, and soil contaminants. It also discusses the use of soil spectroscopy in precision agriculture, where it can be used to identify spatial variability in soil properties and guide site-specific nutrient management.