Automated Food Recognition for Nutritional Analysis in Dietary Evaluation

Abstract

In today's world, computer vision has made remarkable strides, especially in the realm of recognizing food images. Deep neural networks (DNN) have become a standout choice among various machine learning algorithms for their effectiveness in precisely identifying different food items captured in images. Despite the widespread adoption of DNN-based classification algorithms for food recognition, there are persistent challenges in accurately pinpointing foods due to variations in size, shape, and other defining characteristics. This paper aims to provide a brief overview of how deep learning (DL) is leveraged in food recognition and explores its wide-ranging applications. Additionally, it delves into the utilization of machine learning (ML) to construct a robust model for identifying food in images, using the Fruits and Vegetables Image Recognition Dataset. The results highlight a significant improvement in the accuracy of the proposed model, showcasing an impressive increase of around 95%.