1. Research Progress on Machine Learning and Computer Vision Technology in Food Quality Evaluation
- Author
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HUANG Xiaochen, ZHANG Kaili, LIU Yuanjie, CHEN Hong, HUANG Fenghong, WEI Fang
- Subjects
machine learning ,food quality ,computer vision ,food testing ,Food processing and manufacture ,TP368-456 - 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.
- Published
- 2024
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