Mathematical Modeling and Artificial Intelligence in Smart Food Systems: A Bibliometric and Science Mapping Analysis
DOI:
10.29303/jm.v8i2.12278Published:
2026-06-30Downloads
Abstract
This study analyzes the global development of mathematical modeling and artificial intelligence in smart food systems using bibliometric and science mapping approaches. Data were collected from 561 Scopus-indexed documents published between 1994 and 2026 and analyzed using Bibliometrix and Biblioshiny. The analysis covers publication trends, leading sources, country contributions, trend topics, thematic mapping, thematic evolution, co-occurrence networks, and Multiple Correspondence Analysis. The results show rapid publication growth, particularly after 2020, driven by machine learning, artificial intelligence, precision agriculture, predictive modeling, and smart agriculture. The conceptual structure reveals two dominant paradigms: a data-driven approach centered on AI and machine learning, and a model-driven approach emphasizing mathematical modeling, simulation, optimization, and decision-making. The findings indicate increasing convergence between these paradigms, leading to the proposed concept of Hybrid Intelligent Food Systems, which integrates AI-based prediction with mathematical optimization and interpretability to support adaptive, efficient, and sustainable food systems.
Keywords:
Artificial intelligence bibliometric analysis mathematical modeling machine learning smart food systemsReferences
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