Intelligent Image Classification for Grading Egyptian Cotton Lint
Intelligent Image Classification for Grading Egyptian Cotton Lint
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DOI：
10.20944/preprints202108.0139.v1
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Oliver J. Fisher;A. Rady;A. El-Banna;N. Watson;Haitham H. Emaish
中科院分区：
文献类型：
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作者：
Oliver J. Fisher;A. Rady;A. El-Banna;N. Watson;Haitham H. Emaish
埃及棉花是埃及经济最重要的商品之一，以其质量而闻名全球，目前通过人工检查分级。这具有几个缺点，包括显著的劳动力需求、低的检查效率、以及来自检查条件（诸如光和人的主观性）的影响。目前的工作使用低成本的彩色视觉系统，结合机器学习来预测品种吉萨86、97、90、94和96的棉绒等级。探讨和比较了无监督和有监督的机器学习方法。三种不同的监督学习算法进行了评估：线性判别分析，决策树和集成建模。准确度最高的模型（77.3-98.2%）使用了一种集成建模技术来对埃及棉花等级内的样本进行分类：完全良好，良好，完全良好，良好和完全公平。无监督学习技术k-means表明，在对属于较高质量等级的皮棉进行分类时，更容易发生人为错误，并强调需要一个智能系统来取代人工检查。
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.