AI-driven transformation in food manufacturing: a pathway to sustainable efficiency and quality assurance
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 a...
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| Vydáno v: | Frontiers in nutrition (Lausanne) Ročník 12; s. 1553942 |
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| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Switzerland
Frontiers Media S.A
13.03.2025
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| ISSN: | 2296-861X, 2296-861X |
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| Abstract | 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. |
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| AbstractList | 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. 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.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. |
| Author | Goktas, Polat Beecks, Christian Agrawal, Kushagra Kumar, Navneet Holtkemper, Maike |
| AuthorAffiliation | 3 Faculty of Mathematics and Computer Science, FernUniversität in Hagen , Hagen , Germany 1 School of Computer Engineering, KIIT Deemed to be University , Bhubaneswar , India 4 ESM Division, ICAR - National Academy of Agricultural Research Management , Hyderabad , India 2 UCD School of Computer Science and CeADAR, University College Dublin , Belfield, Dublin , Ireland |
| AuthorAffiliation_xml | – name: 3 Faculty of Mathematics and Computer Science, FernUniversität in Hagen , Hagen , Germany – name: 2 UCD School of Computer Science and CeADAR, University College Dublin , Belfield, Dublin , Ireland – name: 4 ESM Division, ICAR - National Academy of Agricultural Research Management , Hyderabad , India – name: 1 School of Computer Engineering, KIIT Deemed to be University , Bhubaneswar , India |
| Author_xml | – sequence: 1 givenname: Kushagra surname: Agrawal fullname: Agrawal, Kushagra – sequence: 2 givenname: Polat surname: Goktas fullname: Goktas, Polat – sequence: 3 givenname: Maike surname: Holtkemper fullname: Holtkemper, Maike – sequence: 4 givenname: Christian surname: Beecks fullname: Beecks, Christian – sequence: 5 givenname: Navneet surname: Kumar fullname: Kumar, Navneet |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40181942$$D View this record in MEDLINE/PubMed |
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| Copyright | Copyright © 2025 Agrawal, Goktas, Holtkemper, Beecks and Kumar. Copyright © 2025 Agrawal, Goktas, Holtkemper, Beecks and Kumar. 2025 Agrawal, Goktas, Holtkemper, Beecks and Kumar |
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| Keywords | resource optimization quality assurance circular economy food manufacturing predictive analytics waste management artificial intelligence |
| Language | English |
| License | 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) and the copyright owner(s) 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. |
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| Title | AI-driven transformation in food manufacturing: a pathway to sustainable efficiency and quality assurance |
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