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Applications of Machine Vision, Image Analysis and Artificial Intelligence in...
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Applications of Machine Vision, Image Analysis and Artificial Intelligence in Food Technology

A special issue of Foods (ISSN 2304-8158). This special issue belongs to the section "Food Engineering and Technology".

Deadline for manuscript submissions: closed (31 October 2025) | Viewed by 3144

Special Issue Editors

Dr. Jianlei Kong

E-Mail Website
Guest Editor
1. National Engineering Research Center for Agricultural Product Quality Safety and Traceability Technology and Application, Beijing 100048, China
2. School of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China
Interests: agricultural product supply chain traceability; food quality and safety assurance; time-series prediction; pattern recognition; deep learning; blockchain traceability
Special Issues, Collections and Topics in MDPI journals
Dr. Panagiotis Tsakanikas

E-Mail Website
Guest Editor
Institute of Communication and Computer Systems, National Technical University of Athens, 15773 Athens, Greece
Interests: signal/image processing and analysis; statistical analysis; sensors analytics; machine learning and AI; food microbiological analysis; food quality assessment; food safety assessment

Special Issue Information

Dear Colleagues,

Food technology serves as a cornerstone in addressing global challenges of food security, sustainability, and nutritional equity. With an exponentially growing population and escalating climate pressures, this field is tasked with optimizing agricultural yields, minimizing post-harvest losses, and ensuring safe, nutrient-dense food distribution. Traditional methodologies, however, often lack the precision and scalability required for modern agri-food systems. This gap is being bridged by transformative technologies such as machine vision, image analysis, and artificial intelligence (AI), which enable data-driven decision-making across the food value chain. AI-powered predictive models enhance shelf-life estimation and supply chain logistics through real-time environmental and biochemical data integration. Furthermore, robotic systems guided by computer vision ensure hygienic, high-speed sorting and packaging, while neural networks optimize processing parameters to reduce energy and water consumption. These innovations not only elevate food safety standards but also align with circular economy principles by curbing waste and resource inefficiency. As the nexus of computational science and food engineering strengthens, these technologies emerge as indispensable tools for building climate-resilient, equitable, and sustainable food systems worldwide. By synergizing high-dimensional visual data with deep learning frameworks, these technologies achieve sub-pixel accuracy in terms of texture classification, the spatial–temporal modeling of spoilage dynamics, and the robotic automation of post-harvest processing. Therefore, contributors are urged to critically evaluate model generalizability, computational efficiency, and translational barriers to foster adoption across heterogeneous food ecosystems, from smallholder farms to smart food processing 4.0 infrastructures.

Dr. Jianlei Kong
Dr. Panagiotis Tsakanikas
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 250 words) can be sent to the Editorial Office for assessment.

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

  • food safety supervision
  • food supply chain traceability
  • machine and computer vision
  • image detection and segmentation
  • artificial intelligence technology
  • multimodal information fusion
  • large-scale model
  • food science and technology

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Published Papers (2 papers)

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Research

13 pages, 2027 KB  
Article
An Improved Diffusion Model for Generating Images of a Single Category of Food on a Small Dataset
by Zitian Chen, Zhiyong Xiao, Dinghui Wu and Qingbing Sang
Foods 2026, 15(3), 443; https://doi.org/10.3390/foods15030443 - 26 Jan 2026
Viewed by 869
Abstract
In the era of the digital food economy, high-fidelity food images are critical for applications ranging from visual e-commerce presentation to automated dietary assessment. However, developing robust computer vision systems for food analysis is often hindered by data scarcity for long-tail or regional [...] Read more.
In the era of the digital food economy, high-fidelity food images are critical for applications ranging from visual e-commerce presentation to automated dietary assessment. However, developing robust computer vision systems for food analysis is often hindered by data scarcity for long-tail or regional dishes. To address this challenge, we propose a novel high-fidelity food image synthesis framework as an effective data augmentation tool. Unlike generic generative models, our method introduces an Ingredient-Aware Diffusion Model based on the Masked Diffusion Transformer (MaskDiT) architecture. Specifically, we design a Label and Ingredients Encoding (LIE) module and a Cross-Attention (CA) mechanism to explicitly model the relationship between food composition and visual appearance, simulating the “cooking” process digitally. Furthermore, to stabilize training on limited data samples, we incorporate a linear interpolation strategy into the diffusion process. Extensive experiments on the Food-101 and VireoFood-172 datasets demonstrate that our method achieves state-of-the-art generation quality even in data-scarce scenarios. Crucially, we validate the practical utility of our synthetic images: utilizing them for data augmentation improved the accuracy of downstream food classification tasks from 95.65% to 96.20%. This study provides a cost-effective solution for generating diverse, controllable, and realistic food data to advance smart food systems. Full article
(This article belongs to the Special Issue Applications of Machine Vision, Image Analysis and Artificial Intelligence in Food Technology)
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20 pages, 12343 KB  
Article
Geographical Origin Identification of Dendrobium officinale Using Variational Inference-Enhanced Deep Learning
by Changqing Liu, Fan Cao, Yifeng Diao, Yan He and Shuting Cai
Foods 2025, 14(19), 3361; https://doi.org/10.3390/foods14193361 - 28 Sep 2025
Cited by 1 | Viewed by 1788
Abstract
Dendrobium officinale is an important medicinal and edible plant in China, widely used in the dietary health industry and pharmaceutical field. Due to the different geographical origins and cultivation methods, the nutritional value, medicinal quality, and price of Dendrobium are significantly different, and [...] Read more.
Dendrobium officinale is an important medicinal and edible plant in China, widely used in the dietary health industry and pharmaceutical field. Due to the different geographical origins and cultivation methods, the nutritional value, medicinal quality, and price of Dendrobium are significantly different, and accurate identification of the origin is crucial. Current origin identification relies on expert judgment or requires costly instruments, lacking an efficient solution. This study proposes a Variational Inference-enabled Data-Efficient Learning (VIDE) model for high-precision, non-destructive origin identification using a small number of image samples. VIDE integrates dual probabilistic networks: a prior network generating latent feature prototypes and a posterior network employing variational inference to model feature distributions via mean and variance estimators. This synergistic design enhances intra-class feature diversity while maximizing inter-class separability, achieving robust classification with limited samples. Experiments on a self-built dataset of Dendrobium officinale samples from six major Chinese regions show the VIDE model achieves 91.51% precision, 92.63% recall, and 92.07% F1-score, outperforming state-of-the-art models. The study offers a practical solution for geographical origin identification and advances intelligent quality assessment in Dendrobium officinale. Full article
(This article belongs to the Special Issue Applications of Machine Vision, Image Analysis and Artificial Intelligence in Food Technology)
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