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.