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Artificial Intelligence and Numerical Simulation in Food Engineering

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Food Science and Technology".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 355

Special Issue Editors


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Guest Editor
Centre for Business and Industry Transformation, Nottingham Trent University, Nottingham, UK
Interests: food engineering; artificial intelligence; numerical simulation
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Advanced Food Innovation Centre, Sheffield Hallam University, Howard Street, Sheffield S1 1WB, UK
Interests: advanced process control; sustainable food processing; Industry 4.0; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Agriculture, Forest, Food and Environmental Sciences, University of Basilicata, 85100 Potenza, Italy
Interests: energy saving and process analysis; cold storage room; automated systems of storage and packing; computer application in all agricultural activities and food processing; post-harvest and storage technology; packaging systems; food loss
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The integration of Artificial Intelligence (AI) and numerical simulation is revolutionizing food engineering, enabling innovative solutions in process optimization, quality control, and supply chain management. AI-based methods such as machine learning, deep learning, and computer vision enable predictive modeling and real-time decision-making with improved efficiency and product uniformity. Also, numerical simulations, from FEA to CFD, enable accurate analysis of thermal, mechanical, and biochemical processes in food processing. This Special Issue is devoted to documenting state-of-the-art research at the intersection of AI and numerical simulation in food engineering for automating, optimizing, and making more sustainable food processing. We invite submissions that report new applications, theoretical developments, and industrial case studies illustrating the potential of these technologies in transforming the food industry.

We are writing to invite you to make a contribution to the Special Issue titled "Artificial Intelligence and Numerical Simulation in Food Engineering". The issue aims to cover the latest advances in AI-driven modeling, machine learning techniques, and numerical simulation techniques such as computational fluid dynamics (CFD) and finite element analysis (FEA) in food processing and quality control. Original research articles, reviews, and case studies that report innovative solutions for automation, optimization, and sustainability in food engineering are invited. Your research experience in this field would be a valuable addition to the issue, and I would like to invite you to submit your research or collaborate with other researchers.

Dr. Mahdi Rashvand
Dr. Hongwei Zhang
Dr. Francesco Genovese
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. Applied Sciences 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 2400 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
  • machine learning
  • smart sensors
  • digital twins
  • sustainability

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Published Papers (1 paper)

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Research

23 pages, 3752 KiB  
Article
Food Waste Detection in Canteen Plates Using YOLOv11
by João Ferreira, Paulino Cerqueira and Jorge Ribeiro
Appl. Sci. 2025, 15(13), 7137; https://doi.org/10.3390/app15137137 - 25 Jun 2025
Viewed by 184
Abstract
This work presents a Computer Vision (CV) platform for Food Waste (FW) detection in canteen plates exploring a research gap in automated FW detection using CV models. A machine learning methodology was followed, starting with the creation of a custom dataset of canteen [...] Read more.
This work presents a Computer Vision (CV) platform for Food Waste (FW) detection in canteen plates exploring a research gap in automated FW detection using CV models. A machine learning methodology was followed, starting with the creation of a custom dataset of canteen plates images before and after lunch or dinner, and data augmentation techniques were applied to enhance the model’s robustness. Subsequently, a CV model was developed using YOLOv11 to classify the percentage of FW on a plate, distinguishing between edible food items and non-edible discarded material. To evaluate the performance of the model, we used a real dataset as well as three benchmarking datasets with food plates, in which it could be detected waste. For the real dataset, the system achieved a mean average precision (mAP) of 0.343, a precision of 0.62, and a recall of 0.322 on the test set as well as demonstrating high accuracy in classifying waste considering the traditional evaluation metrics on the benchmarking datasets. Given these promising results and the provision of open-source code on a GitHub repository, the platform can be readily utilized by the research community and educational institutions to monitor FW in student meals and proactively implement reduction strategies. Full article
(This article belongs to the Special Issue Artificial Intelligence and Numerical Simulation in Food Engineering)
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