Raw cotton may contain various kinds of trash, such as leaf, bark, and seed coat particles. The content of each of these trash categories is useful information for finding more efficient cleaning processes and predicting the quality of the finished products. This paper addresses the importance of using chromatic and geometric features of trash for trash description, and presents three different clustering methods that automatically classify trash based on the feature measurements. Compared with the geometric attributes of trash, such as size and shape, color attributes are less changeable during harvesting and ginning of cotton and are therefore more reliable and descriptive in categorizing trash. Three clustering methods—sum of squares, fuzzy, and neural network—prove effective for trash classification. Sum of squares clustering and fuzzy clustering require iterative computations and generate comparable classification accuracy. Neural network clustering yields the highest accuracy, but it needs more computational time for network training.
The U.S. cotton classification system has been undergoing significant changes, moving from human classing to the use of precise instruments. Along with this trend, the current research is an effort to develop a new computer vision system to measure detailed trash and color attributes of raw cotton. The system primarily consists of a color ccd camera, xenon flash light, and customized software. In this paper, we introduce a new trash and spot identification method, multidimension thresholding, and the methods for characterizing size, spatial density, shape, and color of trash and spots present in cotton samples. We report on the trash and color measurements of twelve cotton samples, including statistical data and distribution curves, and we compare the results from this system with those from other instruments such as the Spinlab and Motion Control hvi machines and the Minolta Chroma Meter CR-210. Finally, we investigate the influence of trash and spots on cotton color values.
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