Deep food insight: a transfer learning approach for food detection and nutrient estimation through image analysis

Dietary choices have a substantial impact on the health of an individual. This AI-driven research aims to recognize, classify, and estimate the origin and nutrition of food. The proposed system is trained using a diverse dataset containing images of food (101 Classes) in different lights and environmental conditions. In this research a transfer learning approach applied with ResNet and InceptionV3 architectures using their pre-trained weights with finetuning of hyperparameters (Learning rate, Batch size and Optimizer). As a result of this approach, the ability to learn intricate features relevant to food recognition was retained while training rapidly. The system achieves impressive accuracy: 96.6% and 96.1% respectively for food identification, nutrient, and origin estimation. The system accurately recognizes popular foods like pizza, sushi, and salads, even in low light. Furthermore, to provide reliable food information to end users, we have developed a user-friendly web application. The app allows users to upload pictures of their meals to receive nutritional and origin information, empowering them to make healthier choices. This simplifies the process of making informed dietary choices for individuals.