ABSTRACT
The Food Recognition System (FRS) is an advanced technological innovation that combines computer vision and artificial intelligence. In a world characterized by vast culinary diversity, Food Recognition System serves as a powerful tool for automating the identification and categorization of various food items through image analysis. We propose the utilization of VGG30, a highly accurate deep learning model, for food recognition. VGG30 is a convolutional neural network (CNN) specifically designed for image grouping. By training it on a dataset of food images, it can successfully identify a wide range of food items. To employ VGG30 for food recognition, it is necessary to first gather a dataset of food images, with each image labeled to indicate the type of food it represents. Once the dataset has been preprocessed, the VGG30 model can be trained using it. its deep architecture allows it to capture intricate features of different food items, ensuring robust performance even in challenging conditions such as varying lighting, angles, and backgrounds. VGG30 is a reliable choice for accurate food recognition across various domains. Finally, its adaptability allows for fine-tuning to specific tasks or preferences, making it an ideal candidate for implementing the Food Recognition System. After training, an independent test set can be used to assess the model's performance before it is put to use in real-world scenarios.
