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Publishing Language: Chinese | Open Access

Research Progress on Machine Learning and Computer Vision Technology in Food Quality Evaluation

Xiaochen HUANG1 , Kaili ZHANG2, Yuanjie LIU2, Hong CHEN1, Fenghong HUANG1, Fang WEI1,3 ()
Hubei Key Laboratory of Lipid Chemistry and Nutrition, Oil Crops and Lipids Process Technology National & Local Joint Engineering Laboratory, Oil Crops Research Institute, Chinese Academy of Agricultural Sciences, Wuhan 430062, China
Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture and Rural Affairs, College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Hubei Hongshan Laboratory, Wuhan 430070, China
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Abstract

In recent years, with rising concerns over food quality and safety, computer vision technology has gradually attracted attention and begun to be widely used in the field of food quality evaluation. Machine learning technologies such as artificial neural networks (ANN), convolutional neural networks (CNN), and support vector machines (SVM) allow automatic assessment and monitoring of food quality by training on large amounts of food images and related data. Particularly, with the development of deep learning, the computer is now able to more accurately recognize food features such as appearance, shape, and color, thereby allowing food classification, prediction and quality monitoring. In addition to its conventional application in food quality assessment, learning technologies also find application in more complex tasks such as defect detection, foreign object detection, and freshness assessment. These technologies not only improve the efficiency of food production and processing but also reduce errors caused by human factors, thereby ensuring food quality and safety. However, despite the significant progress in the application of learning technologies in food quality assessment, there are still challenges that need to be overcome. For instance, the high cost of acquiring and annotating food image datasets, as well as insufficient data quality and quantity, may affect the performance and generalization ability of models. Furthermore, the interpretability and transparency of models are important issues, especially when explaining or making decisions on food quality assessment results. Therefore, further research is needed to explore how to improve the quality and scale of datasets, optimize the robustness and interpretability of models, and develop more efficient and sustainable food quality assessment systems.

Keywords

machine learningfood qualitycomputer visionfood testing
CLC number: TS207.7 Document code: A Article ID: 1002-6630(2024)12-0001-10

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Food Science
Volume 45 Issue 12,
June 2024
Pages 1-10
DOI: 10.7506/spkx1002-6630-20240131-284
Cite this article:
HUANG X, ZHANG K, LIU Y, et al. Research Progress on Machine Learning and Computer Vision Technology in Food Quality Evaluation. Food Science, 2024, 45(12): 1-10. https://doi.org/10.7506/spkx1002-6630-20240131-284
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