Multimodal Deep Learning-Based Intelligent Food Safety Detection and Traceability System
DOI:
https://doi.org/10.53469/ijomsr.2025.08(03).09Keywords:
Multimodal Deep Learning, Food Safety Detection, Traceability System, Blockchain, Federated Learning, Computer Vision, Natural Language Processing, Sensor Data AnalysisAbstract
Food safety has become a critical global issue, requiring effective solutions to reduce health risks and economic losses. The rapid advancement of artificial intelligence (AI) and deep learning (DL) provides new opportunities to address this challenge. This study presents a multimodal food safety detection system that integrates computer vision (CV), natural language processing (NLP), and sensor data analysis to comprehensively monitor food contamination, quality deterioration, and supply chain security. Specifically, the Swin Transformer model is employed for surface defect detection, while temporal convolutional networks (TCN) predict storage environment conditions. Additionally, blockchain and federated learning technologies are incorporated to establish a secure and efficient data-sharing framework, enabling cross-supply chain collaboration and enhancing traceability accuracy. Experimental results show that the system achieves an accuracy rate of over 98% in food contamination detection and supply chain anomaly monitoring, significantly improving food safety management. This study offers a practical and innovative approach to enhancing intelligent food safety regulation.
References
Garcia, S. N., Osburn, B. I., & Jay-Russell, M. T. (2020). One health for food safety, food security, and sustainable food production. Frontiers in Sustainable Food Systems, 4, 1.
Bao, Q., Xin, Q., Wang, Y., Qian, W., & He, Y. (2024). Exploring ICU Mortality Risk Prediction and Interpretability Analysis Using Machine Learning.
Zhao, R., Hao, Y., & Li, X. (2024). Business Analysis: User Attitude Evaluation and Prediction Based on Hotel User Reviews and Text Mining. arXiv preprint arXiv:2412.16744.
Ziang, H., Zhang, J., & Li, L. (2025). Framework for lung CT image segmentation based on UNet++. arXiv preprint arXiv:2501.02428.
Authority, E. F. S., Cabrera, L. C., Di Piazza, G., Dujardin, B., Marchese, E., & Pastor, P. M. (2024). The 2022 European Union report on pesticide residues in food. EFSA Journal, 22(4), e8753.
Yan, H., Wang, Z., Bo, S., Zhao, Y., Zhang, Y., & Lyu, R. (2024, August). Research on image generation optimization based deep learning. In Proceedings of the International Conference on Machine Learning, Pattern Recognition and Automation Engineering (pp. 194-198).
Ismail, N., & Malik, O. A. (2022). Real-time visual inspection system for grading fruits using computer vision and deep learning techniques. Information Processing in Agriculture, 9(1), 24-37.
Zhang, T., Zhang, B., Zhao, F., & Zhang, S. (2022, April). COVID-19 localization and recognition on chest radiographs based on Yolov5 and EfficientNet. In 2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP) (pp. 1827-1830). IEEE.
Wang, Y., Wen, Y., Wu, X., & Cai, H. (2024). Application of Ultrasonic Treatment to Enhance Antioxidant Activity in Leafy Vegetables. International Journal of Advance in Applied Science Research, 3, 49-58.
Mu, W., Kleter, G. A., Bouzembrak, Y., Dupouy, E., Frewer, L. J., Radwan Al Natour, F. N., & Marvin, H. J. P. (2024). Making food systems more resilient to food safety risks by including artificial intelligence, big data, and internet of things into food safety early warning and emerging risk identification tools. Comprehensive Reviews in Food Science and Food Safety, 23(1), e13296.
Wang, Y., Wen, Y., Wu, X., Wang, L., & Cai, H. (2024). Modulation of Gut Microbiota and Glucose Homeostasis through High-Fiber Dietary Intervention in Type 2 Diabetes Management.
Rejeb, A., Keogh, J. G., Zailani, S., Treiblmaier, H., & Rejeb, K. (2020). Blockchain technology in the food industry: A review of potentials, challenges and future research directions. Logistics, 4(4), 27.
Wang, Z., Yan, H., Wei, C., Wang, J., Bo, S., & Xiao, M. (2024, August). Research on autonomous driving decision-making strategies based deep reinforcement learning. In Proceedings of the 2024 4th International Conference on Internet of Things and Machine Learning (pp. 211-215).
Wang, H., Zhang, G., Zhao, Y., Lai, F., Cui, W., Xue, J., ... & Lin, Y. (2024). RPF-ELD: Regional prior fusion using early and late distillation for breast cancer recognition in ultrasound images.
Wu, X., Sun, Y., & Liu, X. (2024). Multi-class classification of breast cancer gene expression using PCA and XGBoost.
Mo, K., Chu, L., Zhang, X., Su, X., Qian, Y., Ou, Y., & Pretorius, W. (2024). DRAL: Deep reinforcement adaptive learning for multi-UAVs navigation in unknown indoor environment. arXiv preprint arXiv:2409.03930.
Shi, X., Tao, Y., & Lin, S. C. (2024). Deep Neural Network-Based Prediction of B-Cell Epitopes for SARS-CoV and SARS-CoV-2: Enhancing Vaccine Design through Machine Learning. arXiv preprint arXiv:2412.00109.
Xu, K., Mo, X., Xu, X., & Wu, H. (2022). Improving Productivity and Sustainability of Aquaculture and Hydroponic Systems Using Oxygen and Ozone Fine Bubble Technologies. Innovations in Applied Engineering and Technology, 1-8.
Wang, Y., Shen, M., Wang, L., Wen, Y., & Cai, H. (2024). Comparative Modulation of Immune Responses and Inflammation by n-6 and n-3 Polyunsaturated Fatty Acids in Oxylipin-Mediated Pathways.
Wang, Y., Wen, Y., Wu, X., & Cai, H. (2024). Comprehensive Evaluation of GLP1 Receptor Agonists in Modulating Inflammatory Pathways and Gut Microbiota.
Qiao, J. B., Fan, Q. Q., Xing, L., Cui, P. F., He, Y. J., Zhu, J. C., ... & Jiang, H. L. (2018). Vitamin A-decorated biocompatible micelles for chemogene therapy of liver fibrosis. Journal of Controlled Release, 283, 113-125.
Lee, I. K., Xie, R., Luz-Madrigal, A., Min, S., Zhu, J., Jin, J., ... & Ma, Z. (2023). Micromolded honeycomb scaffold design to support the generation of a bilayered RPE and photoreceptor cell construct. Bioactive Materials, 30, 142-153.
Liu, Z., Costa, C., & Wu, Y. (2024). Quantitative Assessment of Sustainable Supply Chain Practices Using Life Cycle and Economic Impact Analysis.
Liu, Z., Costa, C., & Wu, Y. (2024). Leveraging Data-Driven Insights to Enhance Supplier Performance and Supply Chain Resilience.
Yodsanit, N., Shirasu, T., Huang, Y., Yin, L., Islam, Z. H., Gregg, A. C., ... & Wang, B. (2023). Targeted PERK inhibition with biomimetic nanoclusters confers preventative and interventional benefits to elastase-induced abdominal aortic aneurysms. Bioactive materials, 26, 52-63.
Zhu, J., Wu, Y., Liu, Z., & Costa, C. (2025). Sustainable Optimization in Supply Chain Management Using Machine Learning. International Journal of Management Science Research, 8(1).
Zhu, J., Ortiz, J., & Sun, Y. (2024, November). Decoupled Deep Reinforcement Learning with Sensor Fusion and Imitation Learning for Autonomous Driving Optimization. In 2024 6th International Conference on Artificial Intelligence and Computer Applications (ICAICA) (pp. 306-310). IEEE.
Lian, J., & Chen, T. (2024). Research on Complex Data Mining Analysis and Pattern Recognition Based on Deep Learning. Journal of Computing and Electronic Information Management, 12(3), 37-41.
Liu, Z., Costa, C., & Wu, Y. (2024). Data-Driven Optimization of Production Efficiency and Resilience in Global Supply Chains. Journal of Theory and Practice of Engineering Science, 4(08), 23-33.
Smith, A. D., Du, S., & Kurien, A. (2023). Vision transformers for anomaly detection and localisation in leather surface defect classification based on low-resolution images and a small dataset. Applied sciences, 13(15), 8716.