Abstract: In today’s fast-paced lifestyle, many individuals consume food without having proper knowledge of its nutritional content, which often results in unhealthy eating habits, obesity, and lifestyle-related health issues. Manual calculation of calories and nutrients is time-consuming and requires expert knowledge, making it impractical for everyday users.

To overcome these challenges, this project presents an AI-Powered Food Nutrition Analyzer using Image Recognition, which automatically identifies food items from images and provides accurate nutritional information.

The proposed system enables users to upload food images through a simple and intuitive web interface developed using React.js. Once the image is uploaded, it undergoes preprocessing techniques such as resizing, normalization, and noise reduction to enhance image quality.

The processed image is then analyzed using a Convolutional Neural Network (CNN) model, which is trained to recognize various food items with high accuracy. CNN is chosen due to its effectiveness in image classification and feature extraction. After successful food recognition, the system retrieves corresponding nutritional details including calories, proteins, fats, carbohydrates, and vitamins from a structured MongoDB database.

The backend of the application is developed using Python and Flask, which handles image processing, model integration, and communication between the frontend and the database. This architecture ensures fast response time and scalability.
The proposed solution eliminates the need for manual calorie estimation and provides instant nutritional feedback to users. It is especially useful for health-conscious individuals, diet planners, fitness enthusiasts, and people managing specific dietary requirements.

By combining artificial intelligence, deep learning, and web technologies, this project demonstrates an efficient and user-friendly approach to dietary analysis. Overall, the AI-Powered Food Nutrition Analyzer promotes nutritional awareness and encourages healthier food choices, showcasing the practical application of deep learning in real-world healthcare and wellness domains.

Keywords: AI, Image Recognition, Food Nutrition Analysis, Convolutional Neural Network (CNN), Deep Learning, Calorie Estimation, Flask, MongoDB.

Downloads: | DOI: 10.17148/IARJSET.2026.13243