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: 31 October 2025 | Viewed by 28
Special Issue Editor
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
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
Guest Editor
Manuscript Submission Information
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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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