Version 1
: Received: 4 August 2021 / Approved: 5 August 2021 / Online: 5 August 2021 (10:55:26 CEST)
How to cite:
Fisher, O. J.; Rady, A.; El-Banna, A. A. A.; Watson, N. J.; Emaish, H. H. Intelligent Image Classification for Grading Egyptian Cotton Lint. Preprints2021, 2021080139. https://doi.org/10.20944/preprints202108.0139.v1
Fisher, O. J.; Rady, A.; El-Banna, A. A. A.; Watson, N. J.; Emaish, H. H. Intelligent Image Classification for Grading Egyptian Cotton Lint. Preprints 2021, 2021080139. https://doi.org/10.20944/preprints202108.0139.v1
Fisher, O. J.; Rady, A.; El-Banna, A. A. A.; Watson, N. J.; Emaish, H. H. Intelligent Image Classification for Grading Egyptian Cotton Lint. Preprints2021, 2021080139. https://doi.org/10.20944/preprints202108.0139.v1
APA Style
Fisher, O. J., Rady, A., El-Banna, A. A. A., Watson, N. J., & Emaish, H. H. (2021). Intelligent Image Classification for Grading Egyptian Cotton Lint. Preprints. https://doi.org/10.20944/preprints202108.0139.v1
Chicago/Turabian Style
Fisher, O. J., Nicholas J. Watson and Haitham H. Emaish. 2021 "Intelligent Image Classification for Grading Egyptian Cotton Lint" Preprints. https://doi.org/10.20944/preprints202108.0139.v1
Abstract
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.
Keywords
Digital Manufacturing; Sensors; Machine Learning; Industry 4.0; Optical; Cotton Lint; Industrial Digital Technologies
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.