A Systematic Literature Review—AI-Enabled Textile Waste Sorting

Ehsan Faghih, Zahra Saki () and Marguerite Moore
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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

Sustainability, 2025, vol. 17, issue 10, 1-27

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.

Keywords: textiles and apparel; sustainability; waste management; artificial intelligence (AI); recycling; sorting; spectroscopy; hyperspectral imaging (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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