Food Recognition System and Nutrition Tracking Using Convolutional Neural Network Model

A cutting-edge technical advancement that blends artificial intelligence and computer vision is the Food Recognition System (FRS). Food Recognition System is an effective tool for automating the identification and classification of different food products using image analysis in a world with a great deal of culinary diversity. We suggest using the very accurate deep learning model VGG30 for food recognition. Convolutional neural networks (CNNs) like VGG30 are made especially for classifying images. It can accurately identify a variety of food items because it was trained on a dataset of food photos. In order to use VGG30 for food recognition, a collection of food photos must first be produced, with each image labelled with the type of food it depicts. The preprocessed dataset can then be used to train the VGG30 model. Because of its deep architecture, it can capture the fine details of many food products and perform well even in difficult situations with changing backgrounds, lighting, and angles. Second, because of its scalability, VGG30 can manage big datasets with ease, which makes it appropriate for applications where there may be a significant volume and variety of food photos. Furthermore, the model gains from transfer learning, which minimizes the requirement for labelled food data and expedites development. Its interpretability makes it easier to comprehend how it identifies food items, which improves openness and confidence. Furthermore, VGG30 is a dependable option for precise food recognition across a variety of domains because to its broad community support and cutting-edge performance. Lastly, because of its versatility, it can be adjusted to particular jobs or preferences, which makes it a perfect fit for the Food Recognition System.