An Intelligent Approach for Food Standards Prediction Using Machine Learning

Abstract

Abstract: Nowadays, standard intake of healthy food is necessary for keeping a balanced diet to avoid health issues in the human body. This project proposes a food recognition system that uses a convolution neural network as a base model for image prediction and then returns nutrition facts such as calories in the given single food image. Knowing the nutrition content of the food that we are consuming helps in maintaining balanced diet. We have aimed with a variety of food categories, each containing thousands of images, and through machine learning training to achieve higher classification accuracy. Firstly, we have planned to train and optimize a CNN, state-of-art model using Tensorflow, we are using CNN as the convolution layers are tweak able and easy to implement. Second, we adapt our model with GUI features as well as nutrition analysis.