Machine learning and AI techniques

Artificial intelligence (AI) and machine learning (ML) offer transformative potential in soil science by enhancing the ability to analyze large, complex datasets and discern intricate relationships within soil systems. Traditionally, soil science relied on empirical observations and field experiments, but AI and ML now provide advanced tools to analyze data more effectively. This chapter explores six key aspects of AI and ML in soil science: types of ML and AI techniques, data preparation, big data manipulation, data processing techniques, model selection and validation, and data visualization. By leveraging AI and ML, soil scientists can make more informed decisions, optimize resources, and advance precision agriculture and sustainable land management practices. These methodologies foster innovation in soil characterization, monitoring, and predictive modeling, addressing the inherent complexities of soil systems.