A comparison of waste education in schools and colleges across five European cities

The European Union produces over 200 million tonnes of municipal waste each year with 47% being recycled or composted. With the EU reuse and recycling targets set at 55% by 2025 and the introduction of the EU’s Circular Economy Action Plan there has never been more importance placed on waste and recycling education. A three-year transnational project ‘An Erasmus+ Waste Education Initiative’ set out to investigate the level of waste and recycling education (WE) that is currently being delivered in five European cities with a view to develop a range of materials to be used in the classroom extracting the best practice from each. This paper highlights the responses from a questionnaire sent to schools and colleges to determine the baseline of WE currently being delivered in Bucharest, Hamburg, Manchester, Tallinn and Zagreb. Factors such as the local waste and recycling infrastructure and population density were also considered to determine the extent of their influence on the type and availability of WE in the classroom. The findings indicate a wide variation in the amount of WE currently being delivered in the five cities. Increased recycling rates and level of infrastructure have an inverse effect on the level of teacher engagement and involvement in waste management projects does not have an impact on the amount of WE that is present in the curriculum or number of registered Eco-Schools. Time constraints due to other curriculum topics, awareness and lack of resources were the main reasons for not including WE in the classroom

Food Recognition and Food Waste Estimation Using Convolutional Neural Network

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