An integrated conceptual framework for AI-driven fruit supply chain quality management : pathways toward circularity and sustainability
Shrestha, H. M. ; Malik, Mohit LU
; Gahlawat, Vijay Kumar
; Mor, Rahul S.
; Mor, Ashish
and Sharma, Mona
(2025)
In Discover Artificial Intelligence
5.
- Abstract
Agri-food systems globally experience growing difficulties associated with losses after harvesting, irregular quality, and inefficient utilization of resources, particularly within fruit supply chain (FSC). Given the complicated structure in FSCs, it is critical to guarantee quality at all levels to sustain customer satisfaction and optimize the utilization of resources. Conventional inspections and grading techniques are frequently challenging, unreliable, and ineffective, necessitating the urgent implementation of advanced automation. Artificial Intelligence (AI) is now recognized as a revolutionary factor in redefining quality control methodologies within the agri-food sector. Therefore, considering these as research gaps, the... (More)
Agri-food systems globally experience growing difficulties associated with losses after harvesting, irregular quality, and inefficient utilization of resources, particularly within fruit supply chain (FSC). Given the complicated structure in FSCs, it is critical to guarantee quality at all levels to sustain customer satisfaction and optimize the utilization of resources. Conventional inspections and grading techniques are frequently challenging, unreliable, and ineffective, necessitating the urgent implementation of advanced automation. Artificial Intelligence (AI) is now recognized as a revolutionary factor in redefining quality control methodologies within the agri-food sector. Therefore, considering these as research gaps, the research aims to thoroughly analyze evolution in advancements of AI applications in FSCs, concentrating on quality assurance, traceability, and sustainability outcomes, acknowledging the constraints of traditional postharvest methods. The research utilizes a systematic theme synthesizing and conceptual framework methodology to analyze the AI related technological advancements. AI applications are classified into five strategic themes along with potential enablers, challenges, and transformative potential applications. The findings indicates that AI improves decision-making, precision, minimizes operational inefficiencies, and promotes compliance with net-zero and circular economy objectives. Thus, the research proposes a conceptual framework integrating all essential components of FSC, intervention of AI, existing challenges, potential outcomes and future research agenda towards possible development in AI. The research’s contribution includes development of integrated framework bridging all related components with sustainable development. It provides a strategic roadmap for researchers, industry stakeholders, regulators and tech developers, enhancing the discussion on sustainable digital transformation FSC.
(Less)
- author
- Shrestha, H. M.
; Malik, Mohit
LU
; Gahlawat, Vijay Kumar
; Mor, Rahul S.
; Mor, Ashish
and Sharma, Mona
- publishing date
- 2025-12-09
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Agri-food supply chain, Artificial intelligence (AI), Explainable AI (XAI), Fruit quality control, Machine learning, Quality management, Sustainability
- in
- Discover Artificial Intelligence
- volume
- 5
- article number
- 376
- pages
- 22 pages
- publisher
- Springer Nature
- external identifiers
-
- scopus:105024892030
- ISSN
- 2731-0809
- DOI
- 10.1007/s44163-025-00645-7
- language
- English
- LU publication?
- no
- id
- f548045c-dac1-43d9-bd89-3c1204c956e2
- date added to LUP
- 2026-02-09 11:09:34
- date last changed
- 2026-03-06 10:28:25
@article{f548045c-dac1-43d9-bd89-3c1204c956e2,
abstract = {{<p>Agri-food systems globally experience growing difficulties associated with losses after harvesting, irregular quality, and inefficient utilization of resources, particularly within fruit supply chain (FSC). Given the complicated structure in FSCs, it is critical to guarantee quality at all levels to sustain customer satisfaction and optimize the utilization of resources. Conventional inspections and grading techniques are frequently challenging, unreliable, and ineffective, necessitating the urgent implementation of advanced automation. Artificial Intelligence (AI) is now recognized as a revolutionary factor in redefining quality control methodologies within the agri-food sector. Therefore, considering these as research gaps, the research aims to thoroughly analyze evolution in advancements of AI applications in FSCs, concentrating on quality assurance, traceability, and sustainability outcomes, acknowledging the constraints of traditional postharvest methods. The research utilizes a systematic theme synthesizing and conceptual framework methodology to analyze the AI related technological advancements. AI applications are classified into five strategic themes along with potential enablers, challenges, and transformative potential applications. The findings indicates that AI improves decision-making, precision, minimizes operational inefficiencies, and promotes compliance with net-zero and circular economy objectives. Thus, the research proposes a conceptual framework integrating all essential components of FSC, intervention of AI, existing challenges, potential outcomes and future research agenda towards possible development in AI. The research’s contribution includes development of integrated framework bridging all related components with sustainable development. It provides a strategic roadmap for researchers, industry stakeholders, regulators and tech developers, enhancing the discussion on sustainable digital transformation FSC.</p>}},
author = {{Shrestha, H. M. and Malik, Mohit and Gahlawat, Vijay Kumar and Mor, Rahul S. and Mor, Ashish and Sharma, Mona}},
issn = {{2731-0809}},
keywords = {{Agri-food supply chain; Artificial intelligence (AI); Explainable AI (XAI); Fruit quality control; Machine learning; Quality management; Sustainability}},
language = {{eng}},
month = {{12}},
publisher = {{Springer Nature}},
series = {{Discover Artificial Intelligence}},
title = {{An integrated conceptual framework for AI-driven fruit supply chain quality management : pathways toward circularity and sustainability}},
url = {{http://dx.doi.org/10.1007/s44163-025-00645-7}},
doi = {{10.1007/s44163-025-00645-7}},
volume = {{5}},
year = {{2025}},
}