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: 18 January 2025 | Viewed by 1481

Special Issue Editors


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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

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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

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Keywords

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

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Published Papers (1 paper)

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Research

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 1073
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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