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...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-03-01
|
| Series: | Frontiers in Nutrition |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnut.2025.1553942/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850052822205202432 |
|---|---|
| author | Kushagra Agrawal Polat Goktas Maike Holtkemper Christian Beecks Navneet Kumar |
| author_facet | Kushagra Agrawal Polat Goktas Maike Holtkemper Christian Beecks Navneet Kumar |
| author_sort | Kushagra Agrawal |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-dc8fbf588f154979946c8346e9b7e980 |
| institution | DOAJ |
| issn | 2296-861X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Nutrition |
| spelling | doaj-art-dc8fbf588f154979946c8346e9b7e9802025-08-20T02:52:42ZengFrontiers Media S.A.Frontiers in Nutrition2296-861X2025-03-011210.3389/fnut.2025.15539421553942AI-driven transformation in food manufacturing: a pathway to sustainable efficiency and quality assuranceKushagra Agrawal0Polat Goktas1Maike Holtkemper2Christian Beecks3Navneet Kumar4School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, IndiaUCD School of Computer Science and CeADAR, University College Dublin, Belfield, Dublin, IrelandFaculty of Mathematics and Computer Science, FernUniversität in Hagen, Hagen, GermanyFaculty of Mathematics and Computer Science, FernUniversität in Hagen, Hagen, GermanyESM Division, ICAR - National Academy of Agricultural Research Management, Hyderabad, IndiaThis 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.https://www.frontiersin.org/articles/10.3389/fnut.2025.1553942/fullartificial intelligencecircular economyfood manufacturingpredictive analyticsquality assuranceresource optimization |
| spellingShingle | Kushagra Agrawal Polat Goktas Maike Holtkemper Christian Beecks Navneet Kumar AI-driven transformation in food manufacturing: a pathway to sustainable efficiency and quality assurance Frontiers in Nutrition artificial intelligence circular economy food manufacturing predictive analytics quality assurance resource optimization |
| title | AI-driven transformation in food manufacturing: a pathway to sustainable efficiency and quality assurance |
| title_full | AI-driven transformation in food manufacturing: a pathway to sustainable efficiency and quality assurance |
| title_fullStr | AI-driven transformation in food manufacturing: a pathway to sustainable efficiency and quality assurance |
| title_full_unstemmed | AI-driven transformation in food manufacturing: a pathway to sustainable efficiency and quality assurance |
| title_short | AI-driven transformation in food manufacturing: a pathway to sustainable efficiency and quality assurance |
| title_sort | ai driven transformation in food manufacturing a pathway to sustainable efficiency and quality assurance |
| topic | artificial intelligence circular economy food manufacturing predictive analytics quality assurance resource optimization |
| url | https://www.frontiersin.org/articles/10.3389/fnut.2025.1553942/full |
| work_keys_str_mv | AT kushagraagrawal aidriventransformationinfoodmanufacturingapathwaytosustainableefficiencyandqualityassurance AT polatgoktas aidriventransformationinfoodmanufacturingapathwaytosustainableefficiencyandqualityassurance AT maikeholtkemper aidriventransformationinfoodmanufacturingapathwaytosustainableefficiencyandqualityassurance AT christianbeecks aidriventransformationinfoodmanufacturingapathwaytosustainableefficiencyandqualityassurance AT navneetkumar aidriventransformationinfoodmanufacturingapathwaytosustainableefficiencyandqualityassurance |