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Artificial intelligence (AI) is gradually being recognised as an innovative instrument in environmental and soil sciences, providing novel solutions for monitoring, analysis, and decision-making processes. This paper examines at the present state of AI applications in various domains, with a focus on soil science. It demonstrates how intelligent agricultural systems forecast soil texture, calculate soil water content, and optimize irrigation techniques by combining machine learning (ML), deep learning, and hybrid artificial intelligence. Furthermore, high-quality, real-time soil evaluation at various geographical and temporal scales is made possible by the combination of artificial intelligence (AI) with geographic information systems (GIS) and sensor technologies. Along with describing deep learning algorithms used to analyze soil qualities, this research also discusses the challenges, limitations, and ethical dilemmas associated with the application of AI in environmental contexts. By pointing out advancements and shortcomings in the existing literature, this study seeks to direct future research towards more clever, efficient, and sustainable soil management techniques.

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Applications of Machine Learning and Artificial Intelligence in Soil and Environmental Sciences: Methods, Challenges, and Future Perspectives

  • 章节

  • 发表日期：

    2026年

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  • Yassine Al Masmoudi,
  • Anas Tallou,
  • Mariya Ouaissa,
  • Mariyam Ouaissa

摘要

Artificial intelligence (AI) is gradually being recognised as an innovative instrument in environmental and soil sciences, providing novel solutions for monitoring, analysis, and decision-making processes. This paper examines at the present state of AI applications in various domains, with a focus on soil science. It demonstrates how intelligent agricultural systems forecast soil texture, calculate soil water content, and optimize irrigation techniques by combining machine learning (ML), deep learning, and hybrid artificial intelligence. Furthermore, high-quality, real-time soil evaluation at various geographical and temporal scales is made possible by the combination of artificial intelligence (AI) with geographic information systems (GIS) and sensor technologies. Along with describing deep learning algorithms used to analyze soil qualities, this research also discusses the challenges, limitations, and ethical dilemmas associated with the application of AI in environmental contexts. By pointing out advancements and shortcomings in the existing literature, this study seeks to direct future research towards more clever, efficient, and sustainable soil management techniques.