Transforming the Wool Supply Chain with AI for a Data-Driven Approach to Quality Control

The textile and agricultural sectors have made great strides forward with the incorporation of AI into wool sorting systems. Addressing the inefficiencies, inconsistencies, and human errors associated with old approaches, this system allows for accurate and rapid automatic classification of wool quality. To accurately categorise wool, AI-powered models, especially VGG16 and Convolutional Neural Networks (CNN), examine important wool characteristics like colour, fibre diameter, moisture content, and texture. By picking up on nuanced differences that humans would miss, these deep learning systems improve precision. This method improves production efficiency, uniformity, and overall quality control in the wool sector by minimising reliance on hand sorting. Continuous monitoring and remote decision-making are further enabled by real-time data capture and cloud-based processing. All parties involved in the supply chain—from farmers to manufacturers to consumers—stand to gain from the AIpowered wool classification system's scalable, economical, and long-term solution. Improved accuracy and broader use could result from future developments in sensor technology and deep learning algorithms.