Deep Learning in Food Processing and Production: Trends, Innovations and Future Prospects
Shriramulu, Nagaratna, Stephan J, Shashikumar, Manjunatha M K, Chinmayi V
European Journal of Nutrition & Food Safety · pp. 264–273 · Published 23 Oct 2025
10.9734/ejnfs/2025/v17i101881Abstract
This review highlights the transformative role of deep learning in food processing and production. Models like CNNs, GANs, Transformers, and hybrid networks show high accuracy in tasks such as food classification, adulteration detection, and quality assessment. These techniques automate manual inspections, enhancing speed, consistency, and safety. Integration with IoT and edge computing enables real-time monitoring and traceability across food supply chains. Modern architectures like ResNet, EfficientNet, and YOLOv5 frequently exceed 90% accuracy. Challenges such as data imbalance, interpretability, and deployment scalability are discussed. Solutions include transfer learning, data augmentation, and federated learning. Emerging trends like Vision Transformers and sustainable AI models are also explored. This review offers valuable insights for researchers and industry professionals aiming to build intelligent, safe, and efficient food systems.
Cited by 2
Juan Pablo Gaona Hernandez, G. M. Olapade, Ha-Seong Cho · Foods · 2026
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