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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.


Single band images of seven non-brown trash and lint at six representative wavelengths.
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pone.0121969.g007: Single band images of seven non-brown trash and lint at six representative wavelengths.

Mentions: Single-band reflectance images of cotton foreign matter were selected at 6 representative wavelengths from 449 nm to 780 nm (Fig. 6 and Fig. 7). In general, the quality of the images was fairly good for detection and recognition of cotton FM on the lint surface. However, the signal-to-noise ratio (SNR) of the images at 449 nm was relatively lower than at other wavelengths due to the low quantum efficiency of the camera at this wavelength. In addition to the SNR of the camera, the uneven surface of the cotton lint also significantly affected the quality of images of cotton lint (compare the lint area between Fig. 6 and Fig. 7). Although other wavelengths were not shown for the brevity reason, some lint areas looked dark in the whole spectral range. These dark areas were due to the tangled or rugged surface and sometimes were difficult to be differentiated from the dark areas of the real FM samples (e.g. the lint area in Fig. 7). Because of this challenge, the detection and identification of cotton FM could not be performed at the pixel-level in the image. Therefore, the mean spectra of samples were extracted using the ROI method instead of the automated masking to obtain the spectral information.


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

Jiang Y, Li C - PLoS ONE (2015)

Single band images of seven non-brown trash and lint at six representative wavelengths.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0121969.g007: Single band images of seven non-brown trash and lint at six representative wavelengths.
Mentions: Single-band reflectance images of cotton foreign matter were selected at 6 representative wavelengths from 449 nm to 780 nm (Fig. 6 and Fig. 7). In general, the quality of the images was fairly good for detection and recognition of cotton FM on the lint surface. However, the signal-to-noise ratio (SNR) of the images at 449 nm was relatively lower than at other wavelengths due to the low quantum efficiency of the camera at this wavelength. In addition to the SNR of the camera, the uneven surface of the cotton lint also significantly affected the quality of images of cotton lint (compare the lint area between Fig. 6 and Fig. 7). Although other wavelengths were not shown for the brevity reason, some lint areas looked dark in the whole spectral range. These dark areas were due to the tangled or rugged surface and sometimes were difficult to be differentiated from the dark areas of the real FM samples (e.g. the lint area in Fig. 7). Because of this challenge, the detection and identification of cotton FM could not be performed at the pixel-level in the image. Therefore, the mean spectra of samples were extracted using the ROI method instead of the automated masking to obtain the spectral information.

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.