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Machine Learning to Effectively Monitor Status of Food Items in Pantry using Convolution Neural Network Models: Case study to compare Accuracy for Pre-trained and Self trained models
  • Shadeeb Hossain,
  • Shamera Hossain
Shadeeb Hossain

Corresponding Author:[email protected]

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Shamera Hossain


This paper focuses on the concept of convolution neural network for binary classification of food items. Two case studies were primarily focused : (i) identifying "rotten" and "fresh " food items using pre-trained models-VGG, ResNet50 and Xception; (ii) binary classification of "half-filled milk carton" and "completely filled milk carton" with different combination of selftrained neural network layers :dense layer, convolution layer and layer size combinations. In our first case study of binary classification, it was found that VGG had a validation accuracy of 97.5% and Xception produced an "overfitting" tendency. For our second case study, the combination with layer size =125, convolution layer = 3 and dense layer=1 had produced the highest validation accuracy of approximately 97 % and was also able to produce the most accurate prediction with different testing samples. This AI model can be implemented in smart refrigerators to let consumers know the status of their food item more accurately as compared to GoogleNet and NasNetLarge prediction.
16 Jan 2024Submitted to TechRxiv
26 Jan 2024Published in TechRxiv