Food Recognition and Food Waste Estimation Using Convolutional Neural Network
Jelena Lubura, Lato Pezo, Mirela Alina Sandu, Viktoria Voronova, Francesco Donsı̀, Jana Šic Žlabur, Bojan Ribić, Anamarija Peter, Jona Šurić, Ivan Brandić, Marija Klõga, Sanja Ostojić, Gianpiero Pataro, Ana Vîrsta, Ana Elisabeta Oros, Darko Micić, Saša Đurović, Giovanni De Feo, Alessandra Procentese, Neven Voća
Abstract
In this study, an evaluation of food waste generation was conducted, using images taken before and after the daily meals of people aged between 20 and 30 years in Serbia, for the period between 1 January and 31 April in 2022. A convolutional neural network (CNN) was employed for the tasks of recognizing food images before the meal and estimating the percentage of food waste according to the photographs taken. Keeping in mind the vast variates and types of food available, the image recognition and validation of food items present a generally very challenging task. Nevertheless, deep learning has recently been shown to be a very potent image recognition procedure, while CNN presents a state-of-the-art method of deep learning. The CNN technique was implemented to the food detection and food waste estimation tasks throughout the parameter optimization procedure. The images of the most frequently encountered food items were collected from the internet to create an image dataset, covering 157 food categories, which was used to evaluate recognition performance. Each category included between 50 and 200 images, while the total number of images in the database reached 23,552. The CNN model presented good prediction capabilities, showing an accuracy of 0.988 and a loss of 0.102, after the network training cycle. The average food waste per meal, in the frame of the analysis in Serbia, was 21.3%, according to the images collected for food waste evaluation.