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The effectiveness of the deep learning models ResNet50, MobileNetV2, VGG16, InceptionV3, and EfficientNetB0 in identifying the freshness of food is evaluated in this study using visual analysis. In resource-constrained situations, these designs are ideal for automated food quality inspection and real-time freshness monitoring as they offer higher accuracy or computational efficiency. Transform learning are used to develop binary classifiers, which were then trained on a dataset of annotated food photos and assessed for efficiency and accuracy. After undergoing standardized preprocessing, the models’ capacity to differentiate between fresh and stale food in a variety of test photos was evaluated. The results show the advantages and disadvantages of each model. The current stream of research frequently concentrates on generic picture classification tasks instead of the particular difficulties of distinguishing subtle visual differences in food freshness, leaving a gap in knowledge of model robustness under real-world conditions. The study advances deep learning applications in food quality evaluation by offering useful insights for model selection based on operational requirements.

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A Comparative Study of Deep Learning Models for Food Freshness Detection Using Transform Learning

  • 会议论文

  • 发表日期：

    2026年

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  • Aakanksha Jain,
  • Tejaskumar Bhatt,
  • Darshita Kalyani,
  • Jatin Modh,
  • Abhishek Jain

摘要

The effectiveness of the deep learning models ResNet50, MobileNetV2, VGG16, InceptionV3, and EfficientNetB0 in identifying the freshness of food is evaluated in this study using visual analysis. In resource-constrained situations, these designs are ideal for automated food quality inspection and real-time freshness monitoring as they offer higher accuracy or computational efficiency. Transform learning are used to develop binary classifiers, which were then trained on a dataset of annotated food photos and assessed for efficiency and accuracy. After undergoing standardized preprocessing, the models’ capacity to differentiate between fresh and stale food in a variety of test photos was evaluated. The results show the advantages and disadvantages of each model. The current stream of research frequently concentrates on generic picture classification tasks instead of the particular difficulties of distinguishing subtle visual differences in food freshness, leaving a gap in knowledge of model robustness under real-world conditions. The study advances deep learning applications in food quality evaluation by offering useful insights for model selection based on operational requirements.