Calorie Estimation of Food and Beverages using Deep Learning

Obesity, a serious chronic disease, is on the rise as a result of how easily food can be brought to our door steps. People's need for food grew, and at the same time, their anxiety about their nutrition also grew. This study offers an image-based calorie estimation system that asks the user to upload an image of a food item in order to calculate the estimated number of calories in the image. It is a multitasking system that displays weekly information on a user's calorie consumption and the number of calories that must be ingested to prevent obesity-related illnesses like cancer, heart attack, etc. To recognize complex pictures, a collection of food images with 20 classes and 500 images are built in each class. This study has developed a six-layer Convolutional Neural Network (CNN) architecture for the purpose of extracting the traits and classifying the images. The proposed food identification trials had an accuracy of 78.7% during testing and 93.29% throughout training. By using software designed to accurately estimate food calories from still images, users and healthcare experts may be able to more rapidly detect dietary practices and food choices connected to health and health concerns. Calorie calculation has been done by using photographs, however, it is difficult, and there is presently no publicly available program that can conduct both food estimation using images and provide health information about the individual.