Innovative integration of computer vision, IoT, and digital twin in food quality and safety assessment
Ensuring food quality and safety is a key priority for public health and economic stability. Traditional methods of food quality assessment, while effective, are often labor-intensive, destructive or lack traceability and transparency. Recent advances in deep learning and computer vision introduce d...
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| Published in: | Trends in food science & technology Vol. 163; p. 105176 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier Ltd
01.09.2025
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| Subjects: | |
| ISSN: | 0924-2244 |
| Online Access: | Get full text |
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| Summary: | Ensuring food quality and safety is a key priority for public health and economic stability. Traditional methods of food quality assessment, while effective, are often labor-intensive, destructive or lack traceability and transparency. Recent advances in deep learning and computer vision introduce digitally intelligent, cost-effective and automated solutions.
This review presents a typical workflow of deep learning and computer vision, from data acquisition and data preprocessing to model selection, training and evaluation for validation, and summarizes the applications of deep learning and computer vision in different areas of food, such as image classification, object detection, image segmentation, and image generation, as well as model optimization strategies for different tasks. The applications of Internet of Things (IoT), digital twin, computer vision, and deep learning technologies in the food industry are highlighted. In addition, this review also discusses transfer learning and model compression methods, and reviews the applications of lightweight models and embedded systems in the food industry.
The innovative integration of technologies such as computer vision, deep learning, IoT, and digital twin has enhanced food traceability and transparency, and promoted sustainable development. The advancement of cloud computing and big data technologies has promoted the deep integration of these technologies, enabling real-time, accurate and dynamic decision-making in food production. Looking forward to the future, the focus of future research should be placed on improving the availability and quality of labeled datasets, enhancing the interpretability and robustness of model.
•Propose the challenges of deep learning models in interpretability and robustness.•Analyzing transfer learning and lightweight model in deep learning.•The application of DL and CV in food quality and safety is reviewed.•Propose an innovative integration of DL, CV, IoT and digital twin technologies.•Summarize the tasks and representative models of computer vision. |
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| ISSN: | 0924-2244 |
| DOI: | 10.1016/j.tifs.2025.105176 |