AI-BASED FOOD DETECTION SYSTEM WITH NUTRITION ESTIMATION AND PERSONALISED DIET PLANNING USING FULL STACK AND ML

Abstract

The increasing awareness of health and nutrition has created a growing demand for intelligent systems that can assist individuals in making better dietary choices. This project presents an AI-Based Food Detection System with Freshness Analysis, Nutrition Estimation, and Personalized Diet Planning using full-stack development and machine learning techniques. The proposed system leverages computer vision and deep learning models to accurately detect and classify food items from images in real time. In addition to food recognition, the system incorporates freshness analysis by evaluating visual features such as color, texture, and appearance to determine the quality and edibility of food. Nutritional information, including calories, proteins, carbohydrates, and fats, is estimated using integrated food databases and predictive models. Based on the user’s health profile, dietary preferences, and nutritional requirements, the system further generates personalized diet plans to promote healthier eating habits. The full-stack implementation ensures seamless interaction between the front-end user interface and backend processing, enabling real-time data analysis and recommendations. This integrated approach enhances user experience while providing a comprehensive solution for food monitoring, nutrition tracking, and diet management. The system can be effectively applied in healthcare, fitness tracking, smart kitchens, and lifestyle management applications