FOOD RECOGNITION AND CALORIE ESTIMATION USING AI

Abstract

With the increasing focus on health and nutrition, accurately estimating the calorie content of food has become essential for diet management and wellness monitoring. Traditional methods of calorie estimation, such as manual measurement or nutritional labels, are often time-consuming, inaccurate, or unavailable. This paper presents an AI-based system for food recognition and calorie estimation using computer vision and machine learning techniques. The proposed system analyzes images of food to automatically identify the type of dish and estimate its nutritional content, including calories. Convolutional Neural Networks (CNN) are employed for feature extraction and food classification, while regression models or pre-trained nutritional databases are used to estimate calorie values. The system can handle diverse food items, complex presentations, and portion sizes. Experimental results demonstrate that the AI-based approach achieves high accuracy in both food recognition and calorie estimation, outperforming traditional manual methods. This technology provides a convenient, fast, and scalable solution for dietary monitoring, personal health management, and fitness applications.