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

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 influenc...

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Vydáno v:Textile research journal Ročník 93; číslo 11-12; s. 2558 - 2575
Hlavní autoři: Fisher, Oliver J, Rady, Ahmed, El-Banna, Aly AA, Watson, Nicholas J, Emaish, Haitham H
Médium: Journal Article
Jazyk:angličtina
Vydáno: London, England SAGE Publications 01.06.2023
Sage Publications Ltd
Témata:
Algorithms
Artificial neural networks
cameras
Charge coupled devices
Classification
Cluster analysis
Clustering
color
Cotton
Evaluation
fabrics
Gossypium hirsutum
Human error
humans
Image processing
Inspection
Labeling
labor
Learning algorithms
lint cotton
Machine learning
Model accuracy
Neural networks
Probabilistic models
Quality assessment
Sample variance
Staples
Supervised learning
Support vector machines
Unsupervised learning
variance
Vector quantization
Industry 4.0
cotton lint
industrial crop
machine learning
Digital manufacturing
optical imaging
ISSN:0040-5175, 1746-7748, 1746-7748
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Abstract 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.
AbstractList 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.
Author Emaish, Haitham H
Fisher, Oliver J
El-Banna, Aly AA
Rady, Ahmed
Watson, Nicholas J
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  givenname: Oliver J
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  organization: Teagasc Food Research Centre, Ireland
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  givenname: Haitham H
  surname: Emaish
  fullname: Emaish, Haitham H
  organization: Department of Soils and Agricultural Chemistry, Faculty of Agriculture, Saba Basha, Alexandria University, Egypt
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1746-7748
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Issue 11-12
Keywords Industry 4.0
cotton lint
industrial crop
machine learning
Digital manufacturing
optical imaging
Language English
License This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
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Snippet 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...
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SubjectTerms Algorithms
Artificial neural networks
cameras
Charge coupled devices
Classification
Cluster analysis
Clustering
color
Cotton
Evaluation
fabrics
Gossypium hirsutum
Human error
humans
Image processing
Inspection
Labeling
labor
Learning algorithms
lint cotton
Machine learning
Model accuracy
Neural networks
Probabilistic models
Quality assessment
Sample variance
Staples
Supervised learning
Support vector machines
Unsupervised learning
variance
Vector quantization
Title An image processing and machine learning solution to automate Egyptian cotton lint grading
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