Limits...
Detection and discrimination of cotton foreign matter using push-broom based hyperspectral imaging: system design and capability.

Jiang Y, Li C - PLoS ONE (2015)

Bottom Line: The experimental results showed that the mean spectra of all 15 types of cotton foreign matter were different from that of the lint.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.

View Article: PubMed Central - PubMed

Affiliation: College of Engineering, University of Georgia, Athens, Georgia, United States of America.

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.

No MeSH data available.


The mean spectra (black solid line) and standard deviation (error bar) of eight brown trash.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4368643&req=5

pone.0121969.g008: The mean spectra (black solid line) and standard deviation (error bar) of eight brown trash.

Mentions: The mean spectra of cotton lint was clearly different from that of most cotton FM except plastic bag (Fig. 8 and Fig. 9). Although the intensity of plastic bag was lower than that of lint, the difference between them was quite small due to the little absorbed light. Since plastic bag was transparent without pigment, the light shined on the plastic bag pieces was mostly reflected instead of being absorbed. The intensity of the cotton lint was higher than that of most cotton FM in the range from 400 nm to 750 nm but was lower than paper in the whole spectral range. This occurred because most cotton FM contain pigments or chemical components which absorb light in the visible range (400 nm to 750 nm), while the lint fiber is reflective in this range. However, paper was an exception because it is a highly reflective artificial material, and thus its intensity was higher than the lint fiber in the whole spectral range. Therefore, from the mean spectra perspective, all cotton FM on the lint surface could be detected.


Detection and discrimination of cotton foreign matter using push-broom based hyperspectral imaging: system design and capability.

Jiang Y, Li C - PLoS ONE (2015)

The mean spectra (black solid line) and standard deviation (error bar) of eight brown trash.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4368643&req=5

pone.0121969.g008: The mean spectra (black solid line) and standard deviation (error bar) of eight brown trash.
Mentions: The mean spectra of cotton lint was clearly different from that of most cotton FM except plastic bag (Fig. 8 and Fig. 9). Although the intensity of plastic bag was lower than that of lint, the difference between them was quite small due to the little absorbed light. Since plastic bag was transparent without pigment, the light shined on the plastic bag pieces was mostly reflected instead of being absorbed. The intensity of the cotton lint was higher than that of most cotton FM in the range from 400 nm to 750 nm but was lower than paper in the whole spectral range. This occurred because most cotton FM contain pigments or chemical components which absorb light in the visible range (400 nm to 750 nm), while the lint fiber is reflective in this range. However, paper was an exception because it is a highly reflective artificial material, and thus its intensity was higher than the lint fiber in the whole spectral range. Therefore, from the mean spectra perspective, all cotton FM on the lint surface could be detected.

Bottom Line: The experimental results showed that the mean spectra of all 15 types of cotton foreign matter were different from that of the lint.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.

View Article: PubMed Central - PubMed

Affiliation: College of Engineering, University of Georgia, Athens, Georgia, United States of America.

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.

No MeSH data available.