Smart Solutions for a Cleaner Planet - Artificial Intelligence and Machine Learning in Plastic Waste Reduction

Aug 4, 2026

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Artificial intelligence and environmental sustainability form a critical intersection shaped by rapid technological progress and complex global challenges. This research applies machine learning models, including R-CNNs, SSDs, and YOLO, to tackle plastic waste management. By using advanced computing and AI, the study targets improved efficiency for identifying, classifying, and supporting recycling of plastic waste. Performance analysis across multiple ML approaches benchmarks current capabilities and points to pathways for future recycling innovations.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages62

Reading Time2 h 36 min

AccessFree to download

Introduction

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Smart Solutions for a Cleaner Planet: Artificial Intelligence and Machine Learning in Plastic Waste Reduction Edgar Rui Lourenço Couchinho Ramos Supervisors Professor PhD Arminda Guerra Lopes Professor PhD Fábio Mendonça Dissertation submitted to the School of Technology of the Polytechnic Institute of Castelo Branco to fulfil the requirements for obtaining the degree of Master’s in Masters in Engenharia Informática – Area of Specialization in Software Development and Interactive Systems, held under the scientific guidance of Coordinating Professor, PhD Arminda Guerra Lopes of the Polytechnic Institute of Castelo Branco and the Coordinating Professor, PhD Fábio Mendonça of the Madeira University (UMa) and Researcher atthe Interactive Technologies Institute (ITI/LARSyS) and Regional Agency for the Development of Research, Technology and Innovation (ARDITI) . Jury Composition Jury President José Carlos Meireles Monteiro Metrôlho Coordinating Professor of the IT Technical Scientific Unit at EST at IPCB Vowels Steven Lopes Abrantes Coordinating Professor of the IT Department of the Higher School of Technology and Management of the Polytechnic Institute of Viseu. Arlindo Ferreira da Silva Assistant teacher of the IT Technical Scientific Unit at EST at IPCB Arminda da Conceição dos Santos Guerra e Lopes Coordinating Professor of the IT Technical Scientific Unit at EST at IPCB Dedicatory To my marvellous wife, the backbone of my success, and to my kids, Salvador and Íris, who inspire me to be my best version every day. To Fernando Pessoa, who gave me my life motto: Para ser grande, sê inteiro: nada Teu exagera ou exclui. Sê todo em cada coisa. Põe quanto és No mínimo que fazes. Assim em cada lago a lua toda Brilha, porque alta vive. (14/02/1933) Sê inteiro. Serei. Sê todo em cada coisa. Serei. Põe quanto tu és no mínimo que fazes. Porei. Nada teu exagera ou exclui. Nada excluirei. A lua toda brilha porque alta vive. Brilhará. Acknowledgements I want to take a moment to express my gratitude to the people who have supported me during my journey. First and foremost, my wife, Andreia, constantly inspires me with her ideas and serves as my muse. I am forever grateful for her love and encouragement. I would also like to thank my parents, Maria and Manuel, for their unwavering support and guidance throughout my life. They have given me the tools and knowledge necessary to navigate this complex and beautiful world. I am grateful to my in-laws, Lucinda and Luís, for their generosity and kindness and for helping care for my children, Salvador and Íris, while I focused on writing my thesis. My sister and best friend, Nadea, has been a constant source of joy and inspiration. I am grateful to have her by my side. I want to thank my thesis supervisors, Arminda and Fábio, for their energy, dynamism, and irreverence. Their spark and passion for making a difference have been infectious and inspiring. Finally, I would like to express my gratitude to Xavier, Bruna, Bruno, Jared, Hannah, and Mikael, along with many others who have contributed their valuable input, insights, and enthusiasm towards the subject of waste management. And to you, Diana, I know you will read this one day and be happy to see your name here. The world is a truly fascinating place. Abstract Artificial intelligence and environmental sustainability intersection has become a critical exploration domain in the contemporary era marked by rapid technological advancements and complex global challenges. This work focuses on the application of Machine Learning models, such as Regin-based Neural Networks (R-CNNs), Single Shot MultiBox Detectors (SSDs), and You Only Look Once (YOLO), to address the pressing issue of plastic waste management. By leveraging state-of-the-art computing technologies and Artificial Intelligence (AI), this research aims to enhance the efficiency and effectiveness of Plastic Waste (PW) identi

FAQs

Which machine learning models are used to address plastic waste management?

The work applies machine learning models including R-CNN, SSD, and YOLO for identification and classification tasks related to plastic waste.

What problem area does the thesis focus on?

The thesis focuses on improving plastic waste management by enhancing AI-driven identification, classification, and recycling-related processes.

How does the thesis evaluate and position its findings?

It performs systematic performance analysis of different ML models to benchmark the current state of the art and to suggest directions for future innovations in recycling technologies.

Aug 4, 2026

Upload by Maeve