Advances in AI for the Quality Assessment of Agri-Food Products
A special issue of Foods (ISSN 2304-8158). This special issue belongs to the section "Food Analytical Methods".
Deadline for manuscript submissions: 28 November 2025 | Viewed by 111
Special Issue Editors
Interests: optical sensing technology; computer vision; electronic nose; electronic tongue; food quality and safety assessment
Special Issues, Collections and Topics in MDPI journals
Interests: ultrasonic sonochemistry; phytochemistry; bioactive molecules; molecular biology; physicochemistry; spectroscopy; nutrition
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
With global population growth and escalating food safety demands, agri-food product quality assessment is facing the triple challenges of efficiency improvement, standardization, and sustainability. Traditional testing methods (e.g., chemical analysis, manual sensory evaluation) have limitations, including extensive time consumption, sample destruction, and high subjectivity, making it difficult to meet the real-time monitoring needs of modernized supply chains. Breakthroughs in artificial intelligence (AI) have brought about a paradigm shift in this field—the fusion of computer vision, multispectral imaging, electronic nose/tongue sensors, and deep learning has made non-invasive, high-throughput, and high-precision quality assessment possible. Today, AI is widely used in the quality assessment of food/agricultural products, but challenges remain. Current research hotspots cover cross-species quality assessment based on transfer learning, quality prediction models for multimodal data fusion, edge computing-enabled development of on-site inspection devices, and blockchain technology-supported whole-chain quality traceability systems. However, core issues such as insufficient algorithm generalization capability, inefficient learning from small samples, and the heterogeneous processing of sensor data need to be broken through, and there is a strong need for interdisciplinary collaboration to promote theoretical innovation and technology implementation.
We are pleased to invite you to contribute your valuable work to this Special Issue on “Advances in AI for quality assessment of agri-food product”. This Special Issue welcomes original research articles and reviews.
This Special Issue aims to bring together cross-cutting research in the fields of artificial intelligence, agricultural engineering, and food science, focusing on the following directions:
- Intelligent sensing technology innovation: developing low-cost, high-precision sensors and edge AI algorithms to realize real-time field/production line monitoring.
- Multimodal data analysis framework: fusing multi-dimensional data such as images, spectra, mechanics, environment, etc., to construct predictive models.
- Interpretable AI system: breaking through the “black box” limitations of deep learning and establishing a quality decision-making mechanism in line with industry standards.
- Full-chain quality traceability: combining blockchain and IoT technologies to build a digital assessment network from production to consumption.
We look forward to receiving your contributions.
Dr. Yujie Wang
Dr. Mostafa Gouda
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Foods is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- artificial intelligence
- deep learning
- sensor technology
- sustainable monitoring
- generalizable model
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