Abstract:This project offers a new system for rapid and accurate food recognition and calorie estimation, increasingly important for personalized health and nutrition management. It utilizes a fine-tuned VGG16 deep learning model to classify images of common foods (like idly, dosa, rice) effectively. Following classification, the system retrieves calorie information from an Excel-based nutritional database, seamlessly linking image analysis with nutritional data. An intuitive Flask web application allows users to upload food images and instantly receive calorie estimations based on the identified food. This end-to-end system demonstrates the potential of combining deep learning with data-driven nutritional insights, enabling better dietary monitoring and smarter health applications.

| DOI: 10.17148/IARJSET.2025.125356