A review of artificial intelligence and its applications in soil science

Document Type : Review

Authors

1 Soil and Water Research Department; East Azerbaijan Agricultural and Natural Resources Research and Education Center; Agricultural Research, Education and Extension Organization (AREEO); Tabriz; Iran.

2 Undergraduate Student, Department of Soil Science and Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran

3 Faculty Member; Soil and Water Research Department; East Azerbaijan Agricultural and Natural Resources Research and Education Center; Agricultural Research, Education and Extension Organization (AREEO); Tabriz; Iran.

4 Assistant Prof., Department of Soil Science and Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran.

Abstract

Objectives
Artificial Intelligence (AI) has emerged as a transformative tool in the field of soil science, providing capabilities for data analysis, pattern recognition, and decision-making that go beyond those of the traditional methods. Extensive research has focused on developing and applying AI in soil science, driven by the growth of digital data, computational power, and algorithmic advancements. While machine learning (ML) currently dominates AI applications, the field encompasses broader areas including digital image analysis, natural language processing (NLP), expert systems, and knowledge representation. In soil science, AI supports diverse tasks, from predicting soil carbon stocks with ML algorithms to evaluating soil functions via decision support systems, extracting metadata from scientific texts, and analyzing digital soil images. Despite this progress, a precise consensus on AI's definition remains elusive, often conflated with ML, even though its scope extends to computer vision, control theory, and robotics. Early predictions anticipated revolutions in soil science through expert systems and natural language interfaces, some realized as fuzzy logic and decision support applications. Recently revitalized interest in AI owes much to advances in ML and digital convergence, that is, the integration of digital data, databases, the internet, and advanced tools for big data management. Nevertheless, interdisciplinary dialogue among the subfields remains limited, constraining the full exploitation of AI potentials. This review study is dedicated to providing a comprehensive overview of AI’s role and applications in soil science, review of its history and definitions, its classification into key domains, and documentation of its diversity across subdisciplines as well as identification of trends, research gaps, challenges, and future opportunities, with emphasis laid on its overreliance on predictive ML and the need for explainability and knowledge integration.
Methodology
This study is a narrative review derived from an extensive body of scientific literature. For this purpose, systematic searches were conducted in major databases including Scopus, Web of Science, Google Scholar, and the Scientific Information Database (in Persian). The search strategy combined keywords such as “artificial intelligence in soil science”, “machine learning”, “expert systems”, “digital soil mapping”, “natural language processing”, “pedotransfer functions”, “soil image analysis”, and “explainable AI” (all in English) along with Persian equivalents for broader coverage. Inclusion criteria prioritized seminal works on AI history and definitions, foundational studies on soil applications, recent reviews, and empirical papers demonstrating AI techniques under various soil contexts. The information thus collected was structured around a taxonomy classifying AI into three main domains, with critical analysis of applications, algorithms, and limitations. Quantitative trends, such as publication growth, were also considered based on annual AI reports.
 
Results
AI traces its origins to early neural models and foundational conferences, evolving through periods of limited progress to modern breakthroughs in protein structure prediction and image generation. Definitions of AI range from emulating human intelligence to rational decision-making for goal achievement. In soil science, AI applications cover sensing and interacting, reasoning and decision-making, as well as learning and prediction. In sensing and interacting, NLP is used to extract data from legacy soil profiles, computer vision to support soil image classification, proximal and remote sensing with ML to predict soil properties, robotics to enable automated sampling, and explainable AI (XAI) to interpret model outputs using techniques such as Shapley values. Reasoning and decision-making applications include expert systems for soil classification, fuzzy logic for handling uncertainty, decision support systems for conservation planning, and optimization algorithms for land evaluation. Learning and prediction (i.e., the dominant domain) employs ML techniques such as random forests and neural networks for digital soil mapping (DSM), pedotransfer functions (PTFs) for estimating hydraulic properties, and predictive modeling for estimating soil salinity or nutrient content. Across soil subdisciplines, AI has proven effective in DSM, PTF development, image classification, and expert systems. However, the majority of applications remain ML-focused and predictive, such as mapping soil organic carbon, with relatively few studies emphasizing explainability or integration of pedological knowledge. Exceptions include NLP for meta-analysis and cognitive soil models, which demonstrate its potentials for more holistic AI uses. Persistent challenges involve overemphasis on prediction, reduced model interpretability, and limited synergy across soil science subfields.
Conclusion
AI has profoundly impacted soil science, offering tools for predictiing, interacting, and decision-making, yet its applications remain heavily skewed toward ML-based forecasting. While AI effectively handles big data and complex patterns, as evidenced by DSM and PTF advancements, it often lacks interpretability and domain-specific knowledge integration, resulting in black-box models that might impede scientific understanding. Notable exceptions such as XAI and NLP for legacy data extraction highlight pathways for broader, more holistic applications. Future directions include mining historical soil profile texts to extract new insights, constructing meta-analyses from soil literature using NLP, developing interpretable ML models, and integrating cognitive models for simulating soil processes. Addressing these challenges will require interdisciplinary collaboration, fostering synergies across subfields and embedding pedological expertise into algorithms. Recommendations include establishing standardized benchmarks for AI applications, promoting open-source datasets and tools for reproducibility, investing in XAI to enhance trust and usability, and exploring hybrid approaches that combine AI with traditional methods for sustainable soil management. Responsible AI development, aligned with ethical principles such as bias mitigation, will ensure its contribution to addressing global challenges including climate resilience and food security.

Keywords

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Land Management Journal
Volume 14, Issue 1
August 2026
Pages 75-111

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