Sustainable Textile Defect Management Using IoT and SVM-Based Smart Inspection

Manual intervention is a common problem in conventional fabric inspection procedures, leading to inconsistencies and inefficiencies in fault identification. By combining Support Vector Machine (SVM) algorithms with Internet of Things (IoT) technology, this research presents a new method for managing textile defects. As a solution, it provides a smart fabric inspection system that can automatically identify and categorize textile flaws by combining sensors enabled by the IoT with classification approaches based on SVM. The system employs SVM models to reliably diagnose errors using real-time data from several sensors integrated inside the textile production process. By using a preventative approach to defect management, firms may reduce production costs, improve product quality, and increase overall efficiency. The proposed method is successful and reliable in experimental assessments, making it an attractive alternative for broad implementation in the textile sector. This solution is a huge step forward in improving textile defect management and easing the way to more dependable and efficient production methods since it combines cutting-edge machine learning algorithms with IoT infrastructure.