A laser imaging method for machine vision detection of white contaminants in cotton

Liu, Feng; Su, Zhenwei; He, Xiangcheng; Zhang, Chaoyong; Chen, Mouqin; Qiao, Liyan

doi:10.1177/0040517514530027

Cited by 9 publications

(5 citation statements)

References 18 publications

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“…The results indicated that it's still difficult to detect transparent plastic films although the contrast between object and background was increased effectively. Liu et al [9] used linear laser cross-sectional imaging to distinguish typical white contaminants from cotton. 12 types of white contaminants including semitransparent plastic mulch and white plastic film were used in their experiment.…”

Section: Introductionmentioning

confidence: 99%

Effective Wavelengths Selection of Hyperspectral Images of Plastic Films in Cotton

Zhang

Qiao

et al. 2016

Computer and Computing Technologies in Agriculture IX

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This research was conducted to investigate the application of detecting plastic films in cotton using visible and near-infrared hyperspectral imaging. A line-scan hyperspectral imaging system (326-1100 nm) was used to detect plastic films mixed with cotton which was an important quality issue. Hyperspectral reflectance images were acquired and difference spectra of cotton and plastic films were extracted and analyzed to determine the dominant wavelengths. Also, as one of the most commonly used methods for dimensionality reduction, principal component analysis (PCA) was chosen to process the hyperspectral images. Afterwards, effective wavelengths were selected by analyzing the first three principal components (PCs) and six single-band images at 473.24 nm, 497.29 nm, 530.6 nm, 670.81 nm, 674.71 nm, and 955.68 nm were extracted respectively. Finally, the selected wavelengths were validated to prove the effectiveness. The results indicated that the selected wavelengths could be able to detect plastic films in cotton instead of the whole wavelengths.

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Section: Introductionmentioning

confidence: 99%

Effective Wavelengths Selection of Hyperspectral Images of Plastic Films in Cotton

Zhang

Qiao

et al. 2016

Computer and Computing Technologies in Agriculture IX

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“…The availability and low commercial value of digital cameras and scanning devices have contributed to an increase in the number of analytical methodologies reported in the literature. [18][19][20] Mustac and Li 18 used uorescence images in cotton samples with a digital single-lens reex camera (DSLR) to identify and classify thirteen types of residues present in cotton.…”

Section: Introductionmentioning

confidence: 99%

“…Liu et al 20 proposed a method to distinguish white contaminants (paper, plastic and feathers) present in cotton with white tones by using red line laser images with a power of 50 MW and a wavelength of 650 nm.…”

Section: Introductionmentioning

confidence: 99%

A novel strategy for the classification of naturally colored cotton fibers based on digital imaging and pattern recognition techniques

et al. 2015

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This study proposes the use of digital images and multivariate analysis as an alternative methodology for the classification of naturally colored cotton fiber, according to cultivar type. For this purpose, two groups were evaluated: the first comprised of BRS 200 Marrom and BRS Topázio cultivars, while the second contained BRS Rubi and BRS Safira cultivars. Cotton fiber sample images were obtained using a webcam and the frequency distribution of color indexes on the grayscale, red-green-blue (RGB), hue (H), saturation (S), value (V), and grayscale channels was obtained. Classification models based on linear discriminant analysis (LDA) with prior variable selection by successive projection algorithm (SPA) and stepwise (SW) were used. For the purpose of comparison, partial least squares discriminant analysis (PLS-DA) applied to the full-histogram was also used. For both groups, the best results were achieved using the LDA/SW model, with a correct classification rate (CCR) of 96% for the prediction set using the HSV combination.The proposed method is simple, low cost, does not use a reagent, does not destroy the sample and provides results in a short period of time.

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“…Commonly, the harvested cottons contain some degree of cotton plant-related contaminants and other irregular foreign matter [2,3]. During subsequent ginning and cleaning practices, great efforts have been made to remove these undesired contaminants as much as possible [4].…”

Section: Introductionmentioning

confidence: 99%

Preliminary Study of Determining Trash Components in Lint Cottons by Near Infrared Spectroscopy Technique

Y¹,

Thibodeaux²,

Foulk³

et al. 2014

J Textile Sci Eng

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The transfer of Near Infrared (NIR) calibration models for the determination of total trash, leaf trash and non-leaf trash components in cotton fibers was conducted between two sets of samples. These samples to be analyzed are inhomogeneous in a bulky state whereas the samples used as calibrations were homogeneous in a ground state. The efficacy of the model transfer was evaluated based on instrumental leaf grade readings of " as is " diverse samples, because current-in-use trash tests cannot generate the trash amount for individual trash components. Results indicated that the predictions from the direct model transfer were unreliable, but they might be acceptable after the correction or conversion of original predictions with standard samples.

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A laser imaging method for machine vision detection of white contaminants in cotton

Cited by 9 publications

References 18 publications

Effective Wavelengths Selection of Hyperspectral Images of Plastic Films in Cotton

Effective Wavelengths Selection of Hyperspectral Images of Plastic Films in Cotton

A novel strategy for the classification of naturally colored cotton fibers based on digital imaging and pattern recognition techniques

Preliminary Study of Determining Trash Components in Lint Cottons by Near Infrared Spectroscopy Technique

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