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Development of Digital Equipment and Artificial Intelligence for Sustainable Food...
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Development of Digital Equipment and Artificial Intelligence for Sustainable Food Systems

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

Deadline for manuscript submissions: 30 November 2025 | Viewed by 1166

Special Issue Editors

Dr. Weipeng Zhang

E-Mail Website
Guest Editor
School of Artificial Intelligence, Beijing Technology and Business University, 11 Fucheng Road, Beijing 100048, China
Interests: artificial intelligence; digital equipment; smart food processing; food safety; food quality control; drying technology
Special Issues, Collections and Topics in MDPI journals
Dr. Chang Chen

E-Mail Website
Guest Editor
Department of Food Science, Cornell AgriTech, Cornell University, 630 West North Street, Geneva, NY 14456, USA
Interests: postharvest technologies; industrial hemp; food quality and safety control; food chemistry and physics analysis; mathematical modeling
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Hong-Wei Xiao

E-Mail Website
Guest Editor
College of Engineering, China Agricultural University, Beijing, China
Interests: postharvest technologies; industrial hemp; food quality and safety control; food chemistry and physics analysis; mathematical modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The theme "Development of Digital Equipment and Artificial Intelligence for Sustainable Food Systems" explores the integration of cutting-edge technologies into food production, processing, and distribution to support sustainability goals. As the global food industry faces challenges related to resource scarcity, environmental impact, and increasing consumer demand, digital equipment and artificial intelligence (AI) provide innovative solutions. The advancement of digital tools, such as IoT sensors, automation, and machine learning, is revolutionizing food systems by enhancing efficiency, reducing waste, and promoting more sustainable practices. AI enables predictive analytics, personalized food systems, and the optimization of food processing techniques, ultimately contributing to sustainable agricultural practices and reducing the carbon footprint. This Special Issue will focus on the latest research and technological advancements, offering insights into how AI-driven solutions can reshape food systems for a sustainable future.

Dr. Weipeng Zhang
Dr. Chang Chen
Prof. Dr. Hong-Wei Xiao
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

  • digital equipment
  • artificial intelligence
  • sustainable food systems
  • food production
  • machine learning
  • IoT sensors
  • food processing

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

Published Papers (2 papers)

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Research

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21 pages, 4949 KB  
Article
An Integrated Lightweight Neural Network Design and FPGA-Accelerated Edge Computing for Chili Pepper Variety and Origin Identification via an E-Nose
by Ziyu Guo, Yong Yin, Haolin Gu, Guihua Peng, Xueya Wang, Ju Chen and Jia Yan
Foods 2025, 14(15), 2612; https://doi.org/10.3390/foods14152612 - 25 Jul 2025
Viewed by 337
Abstract
A chili pepper variety and origin detection system that integrates a field-programmable gate array (FPGA) with an electronic nose (e-nose) is proposed in this paper to address the issues of variety confusion and origin ambiguity in the chili pepper market. The system uses [...] Read more.
A chili pepper variety and origin detection system that integrates a field-programmable gate array (FPGA) with an electronic nose (e-nose) is proposed in this paper to address the issues of variety confusion and origin ambiguity in the chili pepper market. The system uses the AIRSENSE PEN3 e-nose from Germany to collect gas data from thirteen different varieties of chili peppers and two specific varieties of chili peppers originating from seven different regions. Model training is conducted via the proposed lightweight convolutional neural network ChiliPCNN. By combining the strengths of a convolutional neural network (CNN) and a multilayer perceptron (MLP), the ChiliPCNN model achieves an efficient and accurate classification process, requiring only 268 parameters for chili pepper variety identification and 244 parameters for origin tracing, with 364 floating-point operations (FLOPs) and 340 FLOPs, respectively. The experimental results demonstrate that, compared with other advanced deep learning methods, the ChiliPCNN has superior classification performance and good stability. Specifically, ChiliPCNN achieves accuracy rates of 94.62% in chili pepper variety identification and 93.41% in origin tracing tasks involving Jiaoyang No. 6, with accuracy rates reaching as high as 99.07% for Xianjiao No. 301. These results fully validate the effectiveness of the model. To further increase the detection speed of the ChiliPCNN, its acceleration circuit is designed on the Xilinx Zynq7020 FPGA from the United States and optimized via fixed-point arithmetic and loop unrolling strategies. The optimized circuit reduces the latency to 5600 ns and consumes only 1.755 W of power, significantly improving the resource utilization rate and processing speed of the model. This system not only achieves rapid and accurate chili pepper variety and origin detection but also provides an efficient and reliable intelligent agricultural management solution, which is highly important for promoting the development of agricultural automation and intelligence. Full article
(This article belongs to the Special Issue Development of Digital Equipment and Artificial Intelligence for Sustainable Food Systems)
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Review

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22 pages, 3506 KB  
Review
Spectroscopic and Imaging Technologies Combined with Machine Learning for Intelligent Perception of Pesticide Residues in Fruits and Vegetables
by Haiyan He, Zhoutao Li, Qian Qin, Yue Yu, Yuanxin Guo, Sheng Cai and Zhanming Li
Foods 2025, 14(15), 2679; https://doi.org/10.3390/foods14152679 - 30 Jul 2025
Viewed by 608
Abstract
Pesticide residues in fruits and vegetables pose a serious threat to food safety. Traditional detection methods have defects such as complex operation, high cost, and long detection time. Therefore, it is of great significance to develop rapid, non-destructive, and efficient detection technologies and [...] Read more.
Pesticide residues in fruits and vegetables pose a serious threat to food safety. Traditional detection methods have defects such as complex operation, high cost, and long detection time. Therefore, it is of great significance to develop rapid, non-destructive, and efficient detection technologies and equipment. In recent years, the combination of spectroscopic techniques and imaging technologies with machine learning algorithms has developed rapidly, providing a new attempt to solve this problem. This review focuses on the research progress of the combination of spectroscopic techniques (near-infrared spectroscopy (NIRS), hyperspectral imaging technology (HSI), surface-enhanced Raman scattering (SERS), laser-induced breakdown spectroscopy (LIBS), and imaging techniques (visible light (VIS) imaging, NIRS imaging, HSI technology, terahertz imaging) with machine learning algorithms in the detection of pesticide residues in fruits and vegetables. It also explores the huge challenges faced by the application of spectroscopic and imaging technologies combined with machine learning algorithms in the intelligent perception of pesticide residues in fruits and vegetables: the performance of machine learning models requires further enhancement, the fusion of imaging and spectral data presents technical difficulties, and the commercialization of hardware devices remains underdeveloped. This review has proposed an innovative method that integrates spectral and image data, enhancing the accuracy of pesticide residue detection through the construction of interpretable machine learning algorithms, and providing support for the intelligent sensing and analysis of agricultural and food products. Full article
(This article belongs to the Special Issue Development of Digital Equipment and Artificial Intelligence for Sustainable Food Systems)
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