Smart Waste Sorting Systems Using AI for Urban Recycling Efficiency

Thesis Abstract

The escalating volume of urban waste coupled with inefficient sorting practices underscores the urgent need for innovative solutions to enhance recycling processes and promote sustainable waste management. This study seeks to develop, evaluate, and optimize a smart waste sorting system driven by artificial intelligence (AI) to improve the accuracy, speed, and sustainability of urban recycling operations. The primary aim is to examine the efficacy of AI-based detection and classification algorithms in automating waste segregation, thereby reducing human error and operational costs. Specific objectives include (1) designing an AI-powered waste sorting prototype integrating computer vision and machine learning techniques; (2) assessing the system’s classification accuracy across different waste categories; (3) evaluating the operational efficiency and scalability of the system in a real-world urban setting; and (4) identifying challenges and opportunities associated with deploying AI-driven sorting technologies in municipal waste management. The research employs a mixed-methods approach grounded in an explanatory sequential design. Quantitative data are collected through experimental trials involving a sample of 500 waste items representative of common urban waste streams, including plastics, metals, paper, and organics. The prototype’s classification performance is analyzed using advanced machine learning models, specifically convolutional neural networks (CNNs), with accuracy metrics, precision, recall, and F1 scores serving as primary indicators. Descriptive statistics and inferential tests, including t-tests and ANOVA, are employed to evaluate differences in classification accuracy across waste categories. Qualitative insights are gathered through semi-structured interviews with 20 municipal waste management officials and operational staff, analyzed via thematic analysis to identify perceived system benefits, barriers, and implementation considerations. The study hypothesizes that the AI-powered waste sorting system will significantly outperform traditional manual sorting in terms of accuracy and operational efficiency. It is anticipated that the CNN models will achieve classification accuracy exceeding 90% across waste categories, thus demonstrating the potential for reliable automation. The findings are expected to reveal that integration of AI technologies can streamline waste collection workflows, reduce cross-contamination of recyclables, and lower operational costs for urban waste management agencies. Additionally, the research foresees identifying key technical, organizational, and policy-related challenges, including data quality issues, system scalability, user acceptance, and infrastructure requirements. This research contributes to existing knowledge by providing empirical evidence on the effectiveness of AI-driven solutions in urban waste management within a developing city context, a relatively under-explored area. It extends theoretical understanding by demonstrating the practical applicability of the Technology Acceptance Model (TAM) and Diffusion of Innovations theory in the context of smart waste technologies. Furthermore, it offers a comprehensive framework for designing, testing, and deploying AI-based waste sorting systems, thereby serving as a blueprint for municipal authorities, technology developers, and policymakers seeking to adopt digital innovations for sustainability. The main conclusion underscores that AI-enhanced waste sorting systems have the potential to revolutionize urban recycling by enabling more precise, rapid, and cost-effective sorting processes. Recommendations include fostering collaborations between municipal governments and technology providers, investing in infrastructure upgrades to support AI integration, and implementing user training programs to enhance system acceptance. The study advocates for further research into integrating IoT sensors and robotics into AI-based waste sorting solutions, as well as longitudinal assessments of system performance and impacts on urban recycling rates. Overall, this research affirms that harnessing artificial intelligence can significantly advance sustainable waste management practices in modern cities.