Location: Cotton Structure and Quality Research
Title: Automatic detection of seed coat fragments in cotton fabrics Authors
|Bugao, Xu -|
Submitted to: Textile Research Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: November 26, 2011
Publication Date: September 27, 2012
Citation: Bel, P., Bugao, X., Boykin, D.L. 2012. Automatic detection of seed coat fragments in cotton fabrics. Textile Research Journal. 82(16):1711-1719. Interpretive Summary: Seed coat neps are dark specks seen on the surface of greige cotton fabrics. Seed coat neps occur after ginning and stay in the fiber from processing to fabric. Large neps in the early process stages are a mixture of fiber entanglements with the presence of large particles of vegetable matter (VM), trash, or seed coat fragments. Seed coat fragments are an industry-wide problem. Fabrics and yarns are often rejected due to high levels of seed coat fragments. Seed coat fragments devalue cottons, especially on the international market. It is important to be able to predict this phenomenon from high-speed measurements of fiber properties so breeders can know whether or not a new variety will be problematic. Before such predictions can be made, however, it is necessary to find a method for accurately quantifying the level of dark specks onfabrics. In this study, an experimental program was undertaken to use a specific system to evaluate dark specks in fabrics and determine the minimum sample size. The image analysis system was able to distinguish the differences between varieties, and proved to be a reliable system for measuring dark specks on fabric. It was found that five images with 4 samples per replication with 3 replications was needed for the minimum sample size.
Technical Abstract: Seed coat fragments (SCF) reduce the marketability of cotton fiber, yarns and fabrics. It is particularly important to measure SCF content in the fabric because they cause severe dyeing and appearance defects. SCF content is greatly affected by cotton varieties, environmental conditions during crop development and mechanical processing, but studying their effects on SCFs in fabrics can be very tedious and time consuming. In this paper, we present an image analysis system for accurate and fast measurement of SCFs in greige fabrics, and the conditions of using the 'system for reliable and repeatable data. In the study, four different US cotton varieties were selected, and processed with regular manufacturing facilities. The relationship between sample size and precision of the image analysis system was determined through statistical analysis. It was found in this study that the minimum sample size for each variety should consist of five camera images with a minimum of 4 fabric samples per variety with 3 replications, which gives a Least Significant Difference (LSD) of 64.54 for dark speck count. Dark speck counts for the four fabrics tested ranged from 267.7 to 659.9. Increasing sample size will lower the LSD.