AI-Driven Smart Food Perception and Nutrition Assessment

Abstract

Maintaining a healthy diet requires accurate knowledge of the food consumed and its nutritional value. However, manually identifying food items and estimating their calorie content can be difficult and time-consuming. This project, "AI-Driven Smart Food Perception and Nutrition Assessment," presents an intelligent system that automatically recognizes food items from images and provides an estimate of their calorie content using deep learning techniques. The proposed framework employs a Convolutional Neural Network (CNN) to learn visual features from food images and classify different food categories with high accuracy. Before model training, the dataset undergoes preprocessing steps such as image resizing, normalization, and shuffling to improve learning performance. After recognizing the food item, the system retrieves its nutritional information from a calorie database and calculates the estimated calorie intake. It also keeps track of the total calories consumed and the remaining daily calorie allowance, helping users monitor their eating habits. The developed application provides a simple graphical interface for food image upload, model prediction, and nutritional assessment. Overall, the proposed system offers an efficient and practical solution for supporting healthier food choices and improving daily dietary management.