Emerging Trends in AI-Based Soil Contamination Monitoring and Prevention

Soil health directly impacts food security, so investigating contaminants is a topic of interest for the anticipatory study of the action–effect correlation. This paper conducts a systematic literature review through seven analyses, identifying researchers’ interest in soil health using artificial intelligence tools. The first study examines the distribution of articles over the years to assess researchers’ interest in soil health, and subsequently, the same analysis is conducted regarding artificial intelligence (AI) methods. Additionally, the productivity of authors, the distribution of articles by country, relevant publications, and the frequency of keywords are analyzed to identify areas of interest associated with soil health. Subsequently, the branches of AI and examples of applications that have already been investigated in the specialized literature are identified, allowing areas that are currently underexplored to be pinpointed. This paper also proposes a specialized analysis using an algorithm specifically developed by the author for this investigation, which evaluates the interdisciplinary potential of the articles analyzed in the literature. In this way, the authors of the present research will propose new research directions that include machine learning, natural language processing, computer visualization, and other artificial intelligence techniques for monitoring soil contaminants. They will also suggest using these tools as preventive measures to minimize the negative impact of contaminants on the soil. The direct consequence is the protection of soil health and its effects on human health.