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
On-line přístup:	Získat plný text
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