VGG-19 and Vision Transformer Enabled Shelf-Life Prediction Model for Intelligent Monitoring and Minimization of Food Waste in Culinary Inventories

Food waste, particularly in the prepared food industry, presents a serious worldwide concern with serious ethical, environmental and socioeconomic implications. In restaurants and catering contexts, traditional inventory and waste management systems frequently lack the versatility and granularity to mitigate spoilage in real-time. The study proposes a sophisticated deep learning framework that predicts the remaining shelf-life of prepared food items using visual input, enabling timely interventions to reduce food waste. The proposed hybrid architecture integrates VGG-19 (Visual Geometry Group 19-layer network) for fine-grained feature extraction with Vision Transformer (ViT) that models contextual degradation patterns and temporal cues. The model operates by analyzing food images at regular intervals and predicting the remaining time before spoilage, enabling proactive decision-making for consumption prioritization. Food images are categorized into four freshness states: Fresh, Fit for Consumption, About to Expire and Expired, enabling the model to monitor real-time conditions. An elaborate dataset with 34 distinct food categories was utilized in the study, achieving outstanding performance with 98% accuracy, 97.5% precision, 97.9% recall and an F1-score of 97.75% and yielded an estimated 84% reduction in food waste. The model stands out for its non-invasive, image-based decision-making and the potential scalability across various food service settings. By offering predictive insights into food degradation and by using only visual data, the study advances the integration of artificial intelligence into sustainable food management.

@article{Thomas2025,
  title     = {VGG-19 and Vision Transformer Enabled Shelf-Life Prediction Model for Intelligent Monitoring and Minimization of Food Waste in Culinary Inventories},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {8},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Bindhya Thomas and Priyanka Surendran},
  doi       = {10.14569/IJACSA.2025.0160860},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160860}
}