## 8 Future Direction  
Building on the insights from current studies, future research into AI applications for the F&T industry should focus on several key areas to drive innovation and sustainability. Firstly, expanding dataset diversity is crucial. Developing comprehensive datasets that encompass a wider range of fabric types, defect categories, and real-world conditions will enhance the generalizability of AI models. This approach ensures that models are robust and applicable across various scenarios, ultimately improving their reliability and effectiveness [64]. Clothing and textiles have almost infinite variability in the types of fibers, yarns, fabric structures, and construction used; therefore, datasets need to include a wider range of clothing and textiles samples. In addition, generative AI models, such as Generative Adversarial Networks (GANs) and diffusion models, can be leveraged to synthesize realistic images of garments under various conditions. These generated images can simulate different types of wear, damage, or aging, providing valuable training data for AI systems tasked with assessing garment quality or predicting durability. This approach not only augments existing datasets but also helps in developing more accurate and resilient models for evaluating the condition of textiles in recycling and reuse contexts. Secondly, enhancing model efficiency is essential for broader adoption, especially in resource-constrained environments. Innovating lightweight and computationally efficient AI models can facilitate their use in diverse settings, from small-scale workshops to large industrial operations. This focus on efficiency not only reduces computational costs but also makes advanced AI technologies more accessible [65]. Integrating multimodal data represents another promising direction. By combining visual data with other modalities, such as spectral or tactile information, researchers can significantly improve defect detection and textile classification accuracy. This multimodal approach leverages the strengths of different data types to provide a more comprehensive analysis of textile properties [66]. Developing real-time processing capabilities is also a priority. Real-time AI systems capable of handling high-throughput textile processing without compromising accuracy can revolutionize the industry. These systems enable immediate detection and correction of defects, enhancing productivity and reducing waste [64]. Establishing collaborative platforms for dataset sharing and model development is vital for fostering innovation and standardization. Such platforms can bring together researchers, industry practitioners, and NGOs, facilitating the exchange of knowledge and resources. This collaboration can accelerate advancements and ensure that best practices are widely adopted [67] and help overcome identified issues such as dataset diversity. Given the global scope of the problem of textile waste, a cooperative, global solution such as open-source datasets and AI models to more efficiently re-circulate textiles and garments is needed. Integrating sustainability metrics into AI models is imperative. By incorporating metrics that assess and optimize the environmental impact of textile recycling and reuse processes, AI can play a pivotal role in promoting sustainable practices. This integration helps in making informed decisions that balance efficiency with environmental responsibility [65]. Finally, whilst there is a great deal of potential for the application of machine vision and AI technology within fashion and textiles for a circular economy, there is a need to incorporate stronger fashion and textiles disciplinary expertise and perspectives to develop technology which supports not only sorting but also ensuring that the sorted materials align to the needs of industry, who will then use, reuse, remake or recycle these textiles.