Food Calorie Detection Using Image Processing and MongoDB Database

With the increasing concern for health and diet management, accurately estimating food calories plays a vital role in promoting healthy lifestyles. This research focuses on developing an automated system that identifies food items from images, retrieves relevant calorie information from a MongoDB database, and presents the data to users through a web-based interface. The proposed system utilizes machine learning and image processing techniques to classify food items and provide nutritional insights. The system leverages convolutional neural networks (CNNs) for food recognition, ensuring high accuracy in classification. The database stores a comprehensive dataset of food items and their respective calorie values, enabling efficient retrieval of nutritional data. A web-based platform is designed to provide a seamless user experience, allowing individuals to upload images of their meals and receive instant calorie estimations.