Abstract: This document presents the development of an intelligent mobile application that uses an Image-Based Recognition System that runs on a Convolutional Neural Network (CNN) to foster sustainable behaviors and promote environmental consciousness. The system will identify and sort different materials that can be recycled such as wood, plastic, fabric, cardboard, and metal using deep learning-based image recognition. The application allows users to take or post pictures of objects after which they are processed and analyzed to determine the type of material in real-time. After this identification, the system creates rule-based suggestions, which offer curated video tutorials and step-by-step instructions on potential upcycling projects. Such customized recommendations do not only prolong the life of materials, but also encourage users to implement creative, environmentally friendly solutions in their everyday life. The application was developed on the basis of the Agile approach, which focuses on the iterative nature of development, flexibility, and constant feedback of real-life users. The data collection integrated both primary data collection techniques, including interviews and surveys to understand the preferences of the users, and secondary data collection techniques, including academic research and existing literature, to make the system consistent with the best practices in technology. The training data consisted of around 25,000 annotated images, namely around 5,000 images per material type, which was processed by the methods of normalization and augmentation to increase the recognition accuracy and reliability. Technically, the application is divided into three main parts: the input part where users post or take pictures, the processing part where the CNN-based classification takes place, and the output part where the users are shown personalized upcycling video suggestions. The front-end of the system was developed in JavaScript and React Native to provide a cross-platform mobile interface, and the back end was developed in Python and TensorFlow to provide machine learning features. PostgreSQL was used to manage databases in a reliable and secure manner. Ethical aspects were given importance such as direct user consent, balanced data set creation, and energy-efficient model training to reduce the environmental impact. In the future, the project will map out the activities of thorough testing and evaluation to further improve the accuracy of detection, usability, and scalability before the full implementation. In general, this smart mobile app shows the possibilities of integrating artificial intelligence, user-centered design, and sustainability principles to solve environmental issues and spread the culture of responsible consumption.
Taglay et al. (Thu,) studied this question.