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ORIGINAL RESEARCH article
Front. Nutr.
Sec. Nutrition and Food Science Technology
Volume 12 - 2025 | doi: 10.3389/fnut.2025.1553942
This article is part of the Research Topic AI-Driven Advances in Personalized Nutrition through Optimization in Food Manufacturing View all articles
AI-Driven Transformation in Food Manufacturing: A Pathway to Sustainable Efficiency and Quality Assurance
Provisionally accepted- 1 School of Computer Engineering, KIIT University, Bhubaneswar, Odisha, India
- 2 School of Computer Science, College of Science, University College Dublin, Dublin, Ireland
- 3 University of Hagen, Hagen, Germany
- 4 National Academy of Agricultural Research Management (ICAR), Hyderabad, Andhra Pradesh, India
This study aims to explore the transformative role of Artificial Intelligence (AI) in food manufacturing by optimizing production, reducing waste, and enhancing sustainability. This review follows a literature review approach, synthesizing findings from peer-reviewed studies published between 2019 and 2024. A structured methodology was employed, including database searches and inclusion/exclusion criteria to assess AI applications in food manufacturing. By leveraging predictive analytics, real-time monitoring, and computer vision, AI streamlines workflows, minimizes environmental footprints, and ensures product consistency. The study examines AI-driven solutions for waste reduction through data-driven modeling and circular economy practices, aligning the industry with global sustainability goals. Additionally, it identifies key barriers to AI adoption—including infrastructure limitations, ethical concerns, and economic constraints—and proposes strategies for overcoming them. The findings highlight the necessity of cross-sector collaboration among industry stakeholders, policymakers, and technology developers to fully harness AI’s potential in building a resilient and sustainable food manufacturing ecosystem.
Keywords: artificial intelligence, Circular economy, Food manufacturing, predictive analytics, Quality Assurance, Resource optimization, Waste Management
Received: 31 Dec 2024; Accepted: 24 Feb 2025.
Copyright: © 2025 Agrawal, Goktas, Holtkemper, Beecks and Kumar. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Christian Beecks, University of Hagen, Hagen, Germany
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.