Implementing Deep Learning to Estimate Food and Beverage Nutrients

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 identification trials had an accuracy of 78.7% during testing and 93.29% throughout training.