A Bibliometric Review of Machine Learning for Sustainable Agri-Food Systems: Evolution, Collaboration Networks, and Future Directions

Global agri-food systems face a critical conflict between the need to feed a growing population and the imperative to mitigate its substantial environmental impact, including 23% of global greenhouse gas emissions and 70% of freshwater withdrawals. This bibliometric review maps the scientific landscape of Machine Learning (ML) research applied to sustainable agri-food systems. Using a structured bibliometric protocol, we analyzed 648 scientific documents from Scopus (2010–2025) to map the evolution, collaborative networks, and thematic trends in this domain. Results reveal a field that has grown exponentially until 2021, primarily driven by contributions from Computer Science (26%) and Engineering (21%), with key publications in journals such as Computers and Electronics in Agriculture (22 papers, 2631 citations). While China and India lead in productivity (80% of top authors), high-impact research remains strongly linked to international collaborations with institutions in the U.S. and EU. Current ML efforts focus on technical optimization—such as precision irrigation, pest detection, and yield prediction—but fall short in addressing social equity and climate resilience. The study concludes that while ML holds significant promise for sustainable agri-food processing and system optimization, future progress depends on overcoming fragmented regional collaborations and integrating holistic frameworks, such as life-cycle assessment, to ensure resilient and equitable food systems.