Abstract
Cotton quality, a major factor determining both cotton profitability and marketability, is affected by not only the overall quantity of but also the type of the foreign matter. Although current commercial instruments can measure the overall amount of the foreign matter, no instrument can differentiate various types of foreign matter. The goal of this study was to develop a hyperspectral imaging system to discriminate major types of foreign matter in cotton lint. A push-broom based hyperspectral imaging system with a custom-built multi-thread software was developed to acquire hyperspectral images of cotton fiber with 15 types of foreign matter commonly found in the U.S. cotton lint. A total of 450 (30 replicates for each foreign matter) foreign matter samples were cut into 1 by 1 cm2 pieces and imaged on the lint surface using reflectance mode in the spectral range from 400-1000 nm. The mean spectra of the foreign matter and lint were extracted from the user-defined region-of-interests in the hyperspectral images. The principal component analysis was performed on the mean spectra to reduce the feature dimension from the original 256 bands to the top 3 principal components. The score plots of the 3 principal components were used to examine clusterization patterns for classifying the foreign matter. These patterns were further validated by statistical tests. The experimental results showed that the mean spectra of all 15 types of cotton foreign matter were different from that of the lint. Nine types of cotton foreign matter formed distinct clusters in the score plots. Additionally, all of them were significantly different from each other at the significance level of 0.05 except brown leaf and bract. The developed hyperspectral imaging system is effective to detect and classify cotton foreign matter on the lint surface and has the potential to be implemented in commercial cotton classing offices.
Highlights
Cotton is an important natural fiber contributing to the economy around the world
The results showed that X-ray was successful to recognize cotton foreign matter (FM) based on the difference in attenuated energy among various FM, this modality was limited by its high cost and risk of radiation accidents
The main goal of this paper was to explore the potential of detecting and classifying cotton FM commonly found in the U.S cotton using hyperspectral images
Summary
Hyperspectral Imaging for Cotton Foreign Matter Detection of global trade in raw cotton. During the mechanical harvest process, cotton lint is mixed with a large quantity of foreign matter (FM), such as leaves, bract, stem, hull, etc., affecting the quality, profitability, and marketability of the cotton lint. The associated FM are diverse in nature and respond differently to textile cleaning and further processing. Leaves only lead a relatively higher loss of the lint during textile cleaning, whereas plastic materials can have the most harmful effect on the quality of the textile products, adversely affecting spinning performance, and showing up as faults in the textile production, especially after dyeing [3]. Cotton FM detection and classification are critical to cotton and textile industry
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