Artificial intelligence (AI) is rapidly affecting soil science by allowing the analysis of large, complex, and heterogeneous datasets that were previously difficult to exploit. The current review synthesizes the recent advances of AI and highlights how these tools are applied in key soil science domains, such as digital soil mapping, soil fertility management, soil moisture prediction, contamination monitoring, soil carbon assessment, and precision agriculture. This study evaluates the performance of different AI methods, showing that techniques such as random forests, neural networks, and convolutional neural networks often outperform traditional methods in capturing non-linear soil-environment. At the same time, it identifies major limitations such as data scarcity, reproducibility, lack of large datasets, uncertainty, and the “black-box” nature of many models. This review concludes that AI has strong potential to support sustainable soil management, but its real-world impact will depend on better data integration, explainability, standardization, and closer collaboration with scientists, technologists, and end-users.
Kikis et al. (Sun,) studied this question.