LLM-Based Pipeline for Structured Knowledge Extraction from Scientific Literature on Heavy Metal Hyperaccumulation

The rapid growth of the body of literature on heavy metal hyperaccumulation in plants has created a critical bottleneck in data synthesis. Manual curation is slow, labor-intensive, and not scalable. To address this issue, we developed an artificial intelligence pipeline that automatically transforms unstructured scientific papers, including text, tables, and figures, into a structured knowledge database. Our system recovers numerical data and extracts key experimental parameters, such as plant species, metal types, concentrations, and growing conditions. This enables on-demand dataset generation. We validated our pipeline by replicating a recently published, manually curated dataset that required seven months of expert effort. Our tool achieved comparable accuracy in minutes per article. We implemented a dual-validation strategy combining standard extraction metrics with a qualitative “LLM-as-a-Judge” fact-checking layer to assess contextual correctness. This revealed that high extraction performance does not guarantee factual reliability, underscoring the necessity of semantic validation in scientific knowledge extraction. The resulting open, reproducible framework accelerates evidence synthesis, supports trend analysis (e.g., metal–plant co-occurrence networks), and provides a scalable solution for data-driven environmental research.