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A Systematic Literature Review—AI-Enabled Textile Waste Sorting

Author

Listed:
  • Ehsan Faghih

    (Department of Textile and Apparel, Technology and Management, North Carolina State University, Raleigh, NC 27607, USA)

  • Zahra Saki

    (Department of Textile and Apparel, Technology and Management, North Carolina State University, Raleigh, NC 27607, USA)

  • Marguerite Moore

    (Department of Textile and Apparel, Technology and Management, North Carolina State University, Raleigh, NC 27607, USA)

Abstract

The textile and apparel industry faces significant sustainability challenges due to the high volume of waste it generates and the limitations of current recycling systems. Automation in textile waste management has emerged as a promising solution to enhance material recovery through accurate and efficient sorting. This systematic literature review, conducted using the PRISMA-guided PSALSAR methodology, examines recent advancements in computer-based sorting technologies applied in textile recycling. This study identifies and evaluates major technological methods often integrated with machine learning, deep learning, or computer vision models. The strengths and limitations of these approaches are discussed, highlighting their impact on classification accuracy, reliability, and scalability. This review emphasizes the need for further research on blended fiber detection, data availability, and hybrid models to advance automated textile waste management and support a sustainable circular economy.

Suggested Citation

  • Ehsan Faghih & Zahra Saki & Marguerite Moore, 2025. "A Systematic Literature Review—AI-Enabled Textile Waste Sorting," Sustainability, MDPI, vol. 17(10), pages 1-27, May.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:10:p:4264-:d:1651487
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    References listed on IDEAS

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    1. Mine Seçkin & Ahmet Çağdaş Seçkin & Pinar Demircioglu & Ismail Bogrekci, 2023. "FabricNET: A Microscopic Image Dataset of Woven Fabrics for Predicting Texture and Weaving Parameters through Machine Learning," Sustainability, MDPI, vol. 15(21), pages 1-18, October.
    2. Giuseppe Bonifazi & Riccardo Gasbarrone & Roberta Palmieri & Silvia Serranti, 2022. "End-of-Life Textile Recognition in a Circular Economy Perspective: A Methodological Approach Based on Near Infrared Spectroscopy," Sustainability, MDPI, vol. 14(16), pages 1-15, August.
    3. Minwoo Park & Jongpil Jeong, 2022. "Design and Implementation of Machine Vision-Based Quality Inspection System in Mask Manufacturing Process," Sustainability, MDPI, vol. 14(10), pages 1-20, May.
    4. Martina Glogar & Slavenka Petrak & Maja Mahnić Naglić, 2025. "Digital Technologies in the Sustainable Design and Development of Textiles and Clothing—A Literature Review," Sustainability, MDPI, vol. 17(4), pages 1-26, February.
    5. Angelika Sita Ouedraogo & Ajay Kumar & Ning Wang, 2023. "Landfill Waste Segregation Using Transfer and Ensemble Machine Learning: A Convolutional Neural Network Approach," Energies, MDPI, vol. 16(16), pages 1-14, August.
    6. Nelly Oelze, 2017. "Sustainable Supply Chain Management Implementation–Enablers and Barriers in the Textile Industry," Sustainability, MDPI, vol. 9(8), pages 1-15, August.
    7. Budi Harsanto & Ina Primiana & Vita Sarasi & Yayan Satyakti, 2023. "Sustainability Innovation in the Textile Industry: A Systematic Review," Sustainability, MDPI, vol. 15(2), pages 1-21, January.
    8. Chandadevi Giri & Yan Chen, 2022. "Deep Learning for Demand Forecasting in the Fashion and Apparel Retail Industry," Forecasting, MDPI, vol. 4(2), pages 1-17, June.
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    Cited by:

    1. Antonius Setyadi & Suharno Pawirosumarto & Alana Damaris, 2025. "Embedding Circular Operations in Manufacturing: A Conceptual Model for Operational Sustainability and Resource Efficiency," Sustainability, MDPI, vol. 17(15), pages 1-29, July.

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