Trash content of raw cotton is a critical quality attribute. Therefore, accurate trash assessment is crucial for evaluating
cotton’s processing and market value. Current technologies, including gravimetric and surface scanning methods, suffer from various limitations. Furthermore, worldwide, the most commonly used method is still human grading. One of the best alternatives to the aforementioned approaches is 2D x-ray imaging since it allows a thorough analysis of contaminants in a very precise and quick manner. The segmentation of trash particles in 2D transmission images is
difficult since the background cotton is not uniform. Furthermore, there is considerable overlap between the gray levels of trash and cotton. We dealt with this problem by characterizing and identifying the background cotton via scale-space filtering, followed by a “background normalization” process that removes the background cotton, while leaving the trash particles intact. Furthermore, we have successfully employed stereo x-ray vision for recovering the depth information of the piled trash in controlled samples. Finally, the proposed technique was tested on 280 cotton radiographs-with
various trash levels-and the results compared favorably to the existing systems of cotton trash evaluation. Given that the approach described here provides the trash mass in real-time, when realized, it will have a wide-spread impact on the cotton industry.
Technologies currently used for cotton contaminant assessment suffer from some fundamental limitations. These limitations result in the misassessment of cotton quality and may have a serious impact on the evaluation of the economic value of the cotton crop. This paper reports on the recent advances in the use of a 3D x-ray microtomographic system that employs image processing and pattern recognition techniques to accurately detect and classify trash present in cotton. The proposed method offers an attractive alternative to existing trash evaluation technologies, because of its ability to produce 3D representations of the samples, to robustly segment the trash from its background, and to accurately classify the contaminant types. This procedure could have a serious impact on the process control technologies (cotton lint cleaning), and indeed on the economic value of cotton.