AI‐Driven Omics for Smart Remediation of Heavy Metal Contaminated Soils

Heavy metal (HM) contamination in agricultural soils threatens food security, soil health, and human well‐being. While phytoremediation offers a sustainable alternative to conventional remediation methods, its efficiency remains limited. Recent advances in artificial intelligence (AI), machine learning (ML), and multiomics technologies (genomics, proteomics, metabolomics) provide transformative opportunities to overcome these limitations. This review highlights the integration of AI‐driven models with multiomics data to optimize phytoremediation strategies. AI enables the prediction of plant–microbe interactions, selection of plant growth‐promoting bacteria (PGPB), and modeling of metal transporter dynamics, thereby enhancing crop tolerance and metal accumulation. By mining large‐scale omics datasets, AI can also identify critical pathways for detoxification and guide precision engineering of plants and microbes. The convergence of AI, ML, and multi‐omics technologies represents a transformative approach to solving the challenge of heavy metal pollution in soils. This integrated framework not only accelerates the development of metal‐resistant crops but also paves the way for a new era of precision remediation, where tailored, data‐driven solutions could revolutionize soil decontamination and lead to more sustainable and resilient agricultural practices.

• Publication year

  2025

• Venue

  Physiologia Plantarum : An International Journal for Plant Biology

• Publication date

  2025-11-01

• Fields of study

  Biology, Agricultural and Food Sciences, Medicine, Environmental Science

• Identifiers
  DOI 10.1111/ppl.70611PMID 41208603
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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