ncts

This presentation delves into the pivotal applications of hyperspectral defect detection technology in the realms of smart agriculture and smart manufacturing. Hyperspectral imaging technology not only extends beyond the limits of human visible spectrum but also meticulously captures distinct spectral attributes of objects, a crucial element for detecting defects in agricultural produce and manufacturing processes. In the domain of smart agriculture, the presentation addresses the significant challenge of comprehensive quality assessment for agricultural products, especially within high-speed production environments. Conventional manual quality inspection methods are not only time-intensive but also susceptible to human errors. The discussion centers on research dedicated to hyperspectral intelligent defect screening. This approach leverages hyperspectral technology to extract reflectance data from agricultural produce, combining it with deep learning techniques for rapid, thorough, and non-invasive quality assessment. This integrated method substantially enhances the efficiency and precision of agricultural product quality management, elevating market competitiveness. Within the context of smart manufacturing, hyperspectral technology is harnessed for real-time defect detection during the manufacturing process, thereby improving quality control. Moreover, it can facilitate quality tracking and monitoring to ensure products consistently meet established standards, thus heightening production efficiency. This presentation delves deeply into the principles and practical applications of hyperspectral defect detection technology, while contemplating its prospective influence on smart agriculture and smart manufacturing. The superiority of hyperspectral technology lies in its capacity to provide copious data and heightened precision, critical for sustaining and boosting the efficiency of contemporary agriculture and manufacturing. Through this advanced technology, our aim is to achieve smarter, more efficient, and sustainable agricultural and manufacturing processes.