Automatic detection of seed coat fragments in cotton fabrics

Abstract

Seed coat fragments (SCFs) 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 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 that the minimum sample size for each variety should consist of five camera images with a minimum of four fabric samples per variety with three 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.