Cotton plays an important role in the U.S. national economy. This commodity can be contaminated by various foreign matter (FM) during harvesting and processing, leading to potential damage to textile products. Current sensing methods can only detect the presence of foreign matter on the surface of cotton, but cannot detect and classify foreign matter that is mixed with and embedded inside the cotton. This research focused on the detection and classification of common foreign matter hidden within the cotton lint by hyperspectral transmittance imaging. The foreign matter were sandwiched by two thin cotton lint webs and the transmittance imaging platform was designed and optimized for the best performance of the transmittance mode.
Figure 1. Hyperspectral transmittance imaging system
Figure 2. The principles of hyperspectral imaging