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Foods
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Artificial Intelligence (AI) and Machine Learning for Foods
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Artificial Intelligence (AI) and Machine Learning for Foods

A special issue of Foods (ISSN 2304-8158). This special issue belongs to the section "Food Analytical Methods".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 3929

Special Issue Editors

Dr. Bosoon Park

E-Mail Website
Guest Editor
United States Department of Agriculture, Agricultural Research Service, U.S. National Poultry Research Center, Athens, GA 30605, USA
Interests: hyperspectral imaging for food safety and quality; food nanotechnology; AI for food safety; NIR spectroscopy; foodborne detection; biosensor
Special Issues, Collections and Topics in MDPI journals
Dr. Jiyoon Yi

E-Mail Website
Guest Editor Assistant
Biosystems and Agricultural Engineering, Colleges of Engineering & Ag Natural Resources, Michigan State University, East Lansing, MI 48824, USA
Interests: food engineering; AI/ML; food safety and security; AI for microscopy; predictive modeling

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) is becoming increasingly integral to advancements in food research, offering precise and efficient solutions to complex challenges and this Special Issue focuses on the application of AI in food safety, quality control, nutrition, and production, presenting research that employs AI techniques to address critical issues in the field. Topics include but are not limited to the applications of machine learning and deep learning methods in foodborne pathogen detection, metagenomics, food quality inspection, 3D printing of food materials, precision nutrition, and food process optimization and validation. This Special Issue aims to highlight the significant impact of AI in food science and engineering research, encouraging the integration of these technologies to advance food safety, quality, nutrition, and sustainability.

Dr. Bosoon Park
Guest Editor

Dr. Jiyoon Yi
Guest Editor Assistant

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
  • machine learning
  • food safety
  • food security
  • quality control
  • precision nutrition
  • food process optimization
  • metagenomics
  • 3D printing
  • sustainability

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

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16 pages, 3019 KiB  
Article
Machine Learning-Based Morphological Classification and Diversity Analysis of Ornamental Pumpkin Seeds
by Sıtkı Ermiş, Uğur Ercan, Aylin Kabaş, Önder Kabaş and Georgiana Moiceanu
Foods 2025, 14(9), 1498; https://doi.org/10.3390/foods14091498 - 25 Apr 2025
Abstract
Ornamental pumpkin (Cucurbita pepo L. var. ovifera) seeds are highly morphologically variable, and their classification is hence a complex task for the seed industry. Efficient and accurate classification is critical for agricultural production, breeding programs, and seed sorting for commerce. This [...] Read more.
Ornamental pumpkin (Cucurbita pepo L. var. ovifera) seeds are highly morphologically variable, and their classification is hence a complex task for the seed industry. Efficient and accurate classification is critical for agricultural production, breeding programs, and seed sorting for commerce. This study employs machine learning models—Random Forest (RF), LightGBM, and k-Nearest Neighbors (KNN)—to classify ornamental pumpkin seeds based on their morphological (mass, elongation, width, thickness) and colorimetric characteristics (L*, a*, b* values from CIELAB color space). Prior to model training, the data set was preprocessed through normalization and balancing to enhance classification performance. In this study, six different types of ornamental pumpkin seeds were used, with a total of 900 (150 each of SDE0619, SDE1020, SDE1620, SDE2621, SDE4521, and SDE7721). The classification performance of the models was evaluated using different metrics, such as Accuracy, Balanced Accuracy, Precision, Recall, F1 Score, Matthews Correlation Coefficient (MCC), and Cohen’s Kappa. Among the tested models, the RF model performed best, with Accuracy of 0.959, Balanced Accuracy of 0.961, Precision (Macro) of 0.962, Recall (Macro) of 0.961, F1 Score (Macro) of 0.961, MCC of 0.951, and Cohen’s Kappa of 0.951. In contrast, the worst classification performance of the tested models was with the KNN model across all the evaluation metrics. These outcomes reflect the potential of machine learning-based approaches for seed classification automation, error minimization in seed classification, and maximization of efficiency in the seed industry. The high classification performance of the Random Forest model with 95.9% accuracy and 0.951 MCC value shows that artificial intelligence-based automatic classification of ornamental pumpkin seeds according to their morphological and colorimetric characteristics can make significant contributions to the seed industry, while the integration of this approach into seed sorting and quality determination processes can enable the creation of effective breeding schemes for optimum seed selection by maximizing the accuracy of agricultural processes. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Machine Learning for Foods)
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27 pages, 12763 KiB  
Article
A Method for Sorting High-Quality Fresh Sichuan Pepper Based on a Multi-Domain Multi-Scale Feature Fusion Algorithm
by Pengjun Xiang, Fei Pan, Xuliang Duan, Daizhuang Yang, Mengdie Hu, Dawei He, Xiaoyu Zhao and Fang Huang
Foods 2024, 13(17), 2776; https://doi.org/10.3390/foods13172776 - 30 Aug 2024
Viewed by 1517
Abstract
Post-harvest selection of high-quality Sichuan pepper is a critical step in the production process. To achieve this, a visual system needs to analyze Sichuan pepper with varying postures and maturity levels. To quickly and accurately sort high-quality fresh Sichuan pepper, this study proposes [...] Read more.
Post-harvest selection of high-quality Sichuan pepper is a critical step in the production process. To achieve this, a visual system needs to analyze Sichuan pepper with varying postures and maturity levels. To quickly and accurately sort high-quality fresh Sichuan pepper, this study proposes a multi-scale frequency domain feature fusion module (MSF3M) and a multi-scale dual-domain feature fusion module (MS-DFFM) to construct a multi-scale, multi-domain fusion algorithm for feature fusion of Sichuan pepper images. The MultiDomain YOLOv8 Model network is then built to segment and classify the target Sichuan pepper, distinguishing the maturity level of individual Sichuan peppercorns. A selection method based on the average local pixel value difference is proposed for sorting high-quality fresh Sichuan pepper. Experimental results show that the MultiDomain YOLOv8-seg achieves an mAP50 of 88.8% for the segmentation of fresh Sichuan pepper, with a model size of only 5.84 MB. The MultiDomain YOLOv8-cls excels in Sichuan pepper maturity classification, with an accuracy of 98.34%. Compared to the YOLOv8 baseline model, the MultiDomain YOLOv8 model offers higher accuracy and a more lightweight structure, making it highly effective in reducing misjudgments and enhancing post-harvest processing efficiency in agricultural applications, ultimately increasing producer profits. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Machine Learning for Foods)
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Review

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39 pages, 3054 KiB  
Review
Applications of Machine Learning in Food Safety and HACCP Monitoring of Animal-Source Foods
by Panagiota-Kyriaki Revelou, Efstathia Tsakali, Anthimia Batrinou and Irini F. Strati
Foods 2025, 14(6), 922; https://doi.org/10.3390/foods14060922 - 8 Mar 2025
Viewed by 1713
Abstract
Integrating advanced computing techniques into food safety management has attracted significant attention recently. Machine learning (ML) algorithms offer innovative solutions for Hazard Analysis Critical Control Point (HACCP) monitoring by providing advanced data analysis capabilities and have proven to be powerful tools for assessing [...] Read more.
Integrating advanced computing techniques into food safety management has attracted significant attention recently. Machine learning (ML) algorithms offer innovative solutions for Hazard Analysis Critical Control Point (HACCP) monitoring by providing advanced data analysis capabilities and have proven to be powerful tools for assessing the safety of Animal-Source Foods (ASFs). Studies that link ML with HACCP monitoring in ASFs are limited. The present review provides an overview of ML, feature extraction, and selection algorithms employed for food safety. Several non-destructive techniques are presented, including spectroscopic methods, smartphone-based sensors, paper chromogenic arrays, machine vision, and hyperspectral imaging combined with ML algorithms. Prospects include enhancing predictive models for food safety with the development of hybrid Artificial Intelligence (AI) models and the automation of quality control processes using AI-driven computer vision, which could revolutionize food safety inspections. However, handling conceivable inclinations in AI models is vital to guaranteeing reasonable and exact hazard assessments in an assortment of nourishment generation settings. Moreover, moving forward, the interpretability of ML models will make them more straightforward and dependable. Conclusively, applying ML algorithms allows real-time monitoring and predictive analytics and can significantly reduce the risks associated with ASF consumption. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Machine Learning for Foods)
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