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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Veröffentlicht in:	Textile research journal Jg. 93; H. 11-12; S. 2558 - 2575
Hauptverfasser:	Fisher, Oliver J, Rady, Ahmed, El-Banna, Aly AA, Watson, Nicholas J, Emaish, Haitham H
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London, England SAGE Publications 01.06.2023 Sage Publications Ltd
Schlagworte:	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
Online-Zugang:	Volltext
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Zusammenfassung:	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.
Bibliographie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	0040-5175 1746-7748 1746-7748
DOI:	10.1177/00405175221145571