Mapping five decades of rice science research: A machine learning-driven bibliometric analysis (1970–2024)

Rice is fundamental to global food and nutritional security, yet the evolution of rice-related research has not been systematically mapped at scale. This study applied an AI-driven bibliometric framework to 99,011 peer-reviewed articles indexed in Scopus (1970 to 2024), integrating natural language processing using term frequency-inverse document frequency (TF-IDF feature extraction) with topic modelling via Latent Dirichlet Allocation (LDA) and network analysis using graph-based clustering. This enabled both thematic structuring of research and identification of global collaboration patterns. Five dominant knowledge domains emerged: (1) soil contamination and heavy metal uptake in rice systems, (2) agricultural productivity and environmental impact, (3) nutritional and functional applications of rice by-products, (4) genotypic diversity and stress adaptation, and (5) genomic and molecular strategies for rice improvement. Temporal dynamics revealed a shift from agronomic yield and soil management research (1970s to 1990s) toward molecular genetics, stress resilience and environmental sustainability in the post-2000 era, with nutritional functionality and by-product utilization emerging only in the last decade. Collaboration mapping showed Asia being led by India, China and Japan as the primary research hubs, while Western institutions frequently connected regional clusters. Although progress was achieved, thematic compartmentalization remained, with limited interdisciplinary collaboration across molecular, agronomic and nutritional domains. By integrating machine learning (ML) and large-scale bibliometrics, this study provides the first systems-level evidence base of rice science, aimed at prioritizing areas for cross-disciplinary research and policy engagement to enhance and accelerate innovations towards resilient, sustainable and nutrition-sensitive food systems.