Innovative integration of computer vision, IoT, and digital twin in food quality and safety assessment
文献类型： 外文期刊
作者： Mengshuai Guo;Xin Lv;Dan Wang;Hong Chen;Fang Wei
作者机构：
关键词： Computer vision;Deep learning;Digital twin;Food quality and safety;IoT
期刊名称： Trends in Food Science and Technology
ISSN： 0924-2244
年卷期： 2025 年 163 卷
页码：
收录情况： SCIE（2025版） ; ; EI（2025版）
摘要： Background: 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. Scope and approach: 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. Key findings and conclusions: 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.
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