Identifying food and calculating calories are key components of encouraging good eating habits and controlling dietary intake. These days, it's easier to create smart systems that can recognize food items from images or videos and calculate their calorie content using artificial intelligence, computer vision, and machine learning. This study gives an overview of the latest methods and tools for calorie counting and food identification. It looks at the challenges of accurately identifying different foods, including complex dishes and foods from various cuisines, and addresses the variables taken into account when determining the calorie content of food items, including ingredient composition, portion size, and nutritional makeup. To enhance user experience and enable comprehensive calorie tracking, it explores real-time processing capabilities, user-friendly interfaces, and integration with other health and fitness platforms. Additionally, it provides a list of potential recipes. The interface developed in the proposed system is currently trained to identify and provide the calories of 100 food items, including basic food items, fruits, vegetables, and processed foods. The research work uses a real-time dataset for training and employs software such as YOLOv8, and TensorFlow for practical implementation.

Artificial Intelligence Computer Vision Machine Learning Calorie Counting Food Identification