Project Summaries

12-175GA  Project Manager: E. M. Barnes

CATEGORIZE TRASH TYPES IN COTTON USING HYPER-SPECTRAL IMAGING TECHNOLOGY

Changying Li, Georgia Cotton Commission

Any trash in cotton lowers its value; however, removing trash often lowers fiber quality and in many cases will result in fiber loss in the cleaning process. Therefore, the ability to measure trash content real time can help gins precisely control cleaning equipment. Also, the ability to classify the types of trash has useful research applications and could lead to full machine classing as identification of extraneous matter is the last task requiring a human classer. The overall objective of this research project was to design and build a sensing tool by using the hyperspectral imaging technology to categorize different types of cotton trash in cotton lint.

In the first year of this two-year project, focus was mainly on the hyperspectral imaging system development, literature search and review, cotton trash sample collection, and some preliminary experiments. A line-scan hyperspectral imaging system was integrated in the Food and Fiber Sensing Laboratory at the University of Georgia. The system consists of a hyperspectral imaging unit, light source, and a motor controlled linear slide. The hyperspectral imaging system can acquire images in the spectral range from 650 nm to 1000 nm. The final hyperspectral image had 1392 pixels in the spatial dimension and 260 pixels in the spectral dimension. A 150W quartz tungsten-halogen light source and two different optical guides were used to illuminate the cotton sample. The light is direct current (DC)-regulated, and the light intensity can be controlled. In the second year, more comprehensive experiments will be conducted, including more trash samples, improve our imaging system, and develop imaging processing algorithms.

 

Project Year: 2012
 

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