Artificial intelligence in soil science

Abstract

Few would disagree that artificial intelligence (AI) holds potential for advancing knowledge and innovation. Over the past decades, substantial research has been devoted to the development and application of AI in soil science. While most of today's AI applications in soil science are related to machine learning (ML), AI also encompasses other fields such as digital image analysis, natural language processing (NLP), expert systems, and knowledge representation. This review aims to provide a comprehensive overview of AI in soil science. A definition of AI that equates intelligence with rationality is provided, followed by a typical classification of AI into the three main domains of sensing and interacting, reasoning and decision-making, and learning and predicting. From this framework, a taxonomy of AI in soil research is derived and serves as a basis for a literature review. The major findings are as follows: i) AI in soil science is diverse, with applications in decision support systems, image classification, prediction with ML and expert systems; ii) AI in soil science is currently almost exclusively characterized by ML; iii) applications of ML are predominantly found in the field of digital soil mapping and for the development of pedotransfer functions; and iv) most AI applications are used for prediction purposes. A few notable exceptions stand apart from mainstream applications, particularly in the realms of NLP, the development of soil cognitive models, and interpretable ML. Based on these findings, I discuss attention points, such as using AI almost exclusively for prediction at the expense of explanation and the lack of integration of soil knowledge in algorithmic AI solutions. I envision that future developments could include the use of AI for text recognition of legacy soil profile data, providing a new source of soil information. Another promising line of research is the language processing of soil texts to build meta-analyses that summarize the growing body of soil science literature. These new applications could foster substantial new contributions to soil science research.