Study on the measurement and evaluation of cotton color using image analysis

Heng, Chong; Chen, Lijun; Shen, Hua; Wang, Fumei

doi:10.1088/2053-1591/aba3e4

Cited by 9 publications

(26 citation statements)

References 19 publications

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“…The design is based on existing cotton color vision systems. [12][13][14][22][23][24][25][26] Images were captured using a CCD sensor 8.1 MP Fuji A850 digital camera (FUJIFILM Corporation, Minato-ku, Tokyo, Japan), which stored the images in JPEG format with dimensions of 2248 pixel Â 3264 pixel. The CCD sensor was mounted 11 cm vertically above the surface of the samples and the inbuilt autofocus capability of the CCD sensor ensured all images were in focus.…”

Section: Color Vision Systemmentioning

confidence: 99%

“…When measuring the cotton lint color from CCD images, the majority of previous work has failed to account for the influence that trash has on the measurement of color values. 23 Recent work has proposed a two-step trash detection algorithm to improve the cotton lint color assessment accuracy by removing the influence of trash on the cotton lint color measurements. 23 To what extent the more accurate color values improve the accuracy of models classifying cotton grades is yet to be determined.…”

mentioning

confidence: 99%

“…23 Recent work has proposed a two-step trash detection algorithm to improve the cotton lint color assessment accuracy by removing the influence of trash on the cotton lint color measurements. 23 To what extent the more accurate color values improve the accuracy of models classifying cotton grades is yet to be determined.…”

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confidence: 99%

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An image processing and machine learning solution to automate Egyptian cotton lint grading

Fisher

Rady

El-Banna

et al. 2022

Textile Research Journal

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Egyptian cotton is one of the most important commodities for the Egyptian economy and is renowned globally for its quality, which is largely assessed and graded by manual inspection. This grading has several drawbacks, including significant labor requirements, low inspection efficiency, and influence from inspection conditions such as light and human subjectivity. This work proposes a low-cost solution to replace manual inspection with classification models to grade Egyptian cotton lint using images captured by a charge-coupled device camera. While this method has been evaluated for classifying US and Chinese upland cotton staples, it has not been tested on Egyptian cotton, which has unique characteristics and grading requirements. Furthermore, the methodology to develop these classification models has been expanded to include image processing techniques that remove the influence of trash on color measurements and extract features that capture the intra-sample variance of the cotton samples. Three different supervised machine learning algorithms were evaluated: artificial neural networks; random forest; and support vector machines. The highest accuracy models (82.13–90.21%) used a random forest algorithm. The models’ accuracy was limited by the human error associated with labeling the cotton samples used to develop the classification models. Unsupervised machine learning methods, including k-means clustering, hierarchical clustering, and Gaussian mixture models, were used to indicate where labeling errors occurred.

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Section: Color Vision Systemmentioning

confidence: 99%

mentioning

confidence: 99%

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An image processing and machine learning solution to automate Egyptian cotton lint grading

Fisher

Rady

El-Banna

et al. 2022

Textile Research Journal

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“…This suggests that this feature alone may not be able to classify cotton lint grade to sufficient accuracies. The three remaining features contain information relating to the colour of the cotton lint, a key cotton property linked to quality [31]. Previous path correlation analysis has identified that colour measurements from a HVI machine show a strong negative correlation between the degree of yellowness (b*) and Egyptian cotton quality, and a strong positive correlation between the degree of reflectance (L*) and Egyptian cotton quality [9].…”

Section: Feature Analysismentioning

confidence: 99%

Intelligent Image Classification for Grading Egyptian Cotton Lint

Fisher¹,

Rady²,

El-Banna³

et al. 2021

Preprint

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Egyptian cotton is one of the most important commodities to the Egyptian economy and is renowned globally for its quality, which is currently graded by manual inspection. This has several drawbacks including significant labour requirement, low inspection efficiency, and influence from inspection conditions such as light and human subjectivity. This current work uses a low-cost colour vision system, combined with machine learning to predict the cotton lint grade of the cultivars Giza 86, 97, 90, 94 and 96. Unsupervised and supervised machine learning approaches were explored and compared. Three different supervised learning algorithms were evaluated: linear discriminant analysis, decision trees and ensemble modelling. The highest accuracy models (77.3-98.2%) used an ensemble modelling technique to classify samples within the Egyptian cotton grades: Fully Good, Good, Fully Good Fair, Good Fair and Fully Fair. The unsupervised learning technique k-means showed that human error is more likely to occur when classifying lint belonging to the higher quality grades and underlined the need for an intelligent system to replace manual inspection.

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“…However, medium-fiber cotton varieties also have their own drawbacks, which are indicators of the quality of the fiber in them, which is shorter, coarser and less durable than fine fiber. Currently, the indicators of cotton fiber are determined by the modern system of measurement HVI-1000 [8,9,10,11]. According to the results, cotton fiber is divided into types and varieties according to the standards.…”

Section: Introductionmentioning

confidence: 99%

A research on productivity of newly created cotton varieties and evaluation of cotton fiber quality indicators

et al. 2023

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This article analyzes the productivity of early maturing and new cotton varieties grown in the country. The study was conducted on early ripening and cotton varieties grown in Jizzakh region. At that time, the highest yield was 37.1% in Bukhara-102 variety, and the lowest yield was 30.0% in Sultan variety. Yields in early maturing new varieties averaged 28.9%. The yield of Porloq-4 was 40.9%, and that of С-6779 was 23.0%. Yields of early maturing varieties averaged 33.3%. Porloq-4 and Bukhara-102 varieties have high indicators of early ripening and yield. Physico-mechanical performance of these cotton varieties was analyzed on the basis of HVI (High Volume Instrument) results. From the results we can see that the specific breaking strength of early maturing and new varieties Porloq-4 and Bukhara-102 in cotton fiber was 37.2%, the upper length was 35.5 mm. Then the specific tensile strength of Porloq-4 cotton fiber is 1.8% lower than the average tensile strength, and the upper average length is 5.1 mm higher. The specific tensile strength of Bukhara-102 fiber is 1.9% higher than the average tensile strength, the average length is 5.3 mm less. The short fiber index averaged 7.5% and the elongation at break was 7.9% on average. The short fiber index of the Porloq-4 variety is 3% lower than the average short fiber index, and the elongation index at break is 1.6% higher than the average. We can see that the index of short fibers of Bukhara-102 fiber is 3% higher than the average, and the elongation at break is 1.5% lower than the average.

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Study on the measurement and evaluation of cotton color using image analysis

Cited by 9 publications

References 19 publications

An image processing and machine learning solution to automate Egyptian cotton lint grading

An image processing and machine learning solution to automate Egyptian cotton lint grading

Intelligent Image Classification for Grading Egyptian Cotton Lint

A research on productivity of newly created cotton varieties and evaluation of cotton fiber quality indicators

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