[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123849-en":3,"doc-seo-123849-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123849,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","An Image Processing and Machine Learning Solution to Automate Egyptian Cotton Lint Grading","Egyptian cotton quality is assessed through manual inspection of lint images, a process that is slow, labor-intensive, and affected by lighting and human subjectivity. The study proposes a low-cost image-processing and supervised machine learning pipeline that replaces manual grading by classifying cotton lint using camera-captured images. The approach is adapted for Egyptian cotton by removing trash interference on color measurements and extracting features reflecting intra-sample variance. Random forest achieved the highest accuracy (82.13–90.21%), while unsupervised clustering methods helped locate labeling errors.","Original article  \nAn image processing and machine learning solution to automate Egyptian cotton lint grading  \nTextile Research Journal  \n2023, Vol. 93(11–12) 2558–2575! The Author(s) 2022  \nArticle reuse guidelines: [sagepub.com/journals-permissions](sagepub.com/journals-permissions)[ ](sagepub.com/journals-permissions)[DOI: 10.1177/00405175221145571](DOI: 10.1177/00405175221145571)[ ](DOI: 10.1177/00405175221145571)[journals.sagepub.com/home/trj](journals.sagepub.com/home/trj)  \nOliver J Fisher 1 , Ahmed Rady 1,2, Aly AA El-Banna3, Nicholas J Watson 1 and Haitham H Emaish4  \nAbstract  \nEgyptian 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.  \nKeywords  \nDigital manufacturing, machine learning, Industry 4.0, optical imaging, cotton lint, industrial crop  \nCotton is an internationally important textile crop, accounting for 90% of all-natural fibers used in the textile industry.1 The textile industry plays a significant role in the Egyptian economy and wider society, contributing around 14% of gross domestic product (GDP)2 and employing 25 . 8% of the industrial workforce.3 However, since the mid-1980s, the production of Egyptian cotton has been declining,3 and between 1980 and 2019 exports have decreased from 164,000 to 71,000 tonnes.4 The industry is subject to various challenges (e.g., fraud and low productivity3), causing domestic5 and international6 strategies to be introduced to strengthen and modernize the Egyptian cotton industry.  \nAn important stage during the cotton production process is the grading of harvested cotton lint to evaluate its economic value, which is determined by its processability (e.g., cleaning requirements) and  \nquality.7 Incorrectly grading the cotton lint results in over-processing, which can lead to cotton fiber breakage, reducing the value of the cotton.8 The most recognized and widely used grade standards are the Universal Upland Grade Standards, which have 25 grades determined by cotton lint color and leaf  \n1 Food Water Waste Research Group, Faculty of Engineering, University of Nottingham, UK  \n2Teagasc Food Research Centre, Ireland  \n3Department of Plant Production, Faculty of Agriculture, Saba Basha, Alexandria University, Egypt  \n4Department of Soils and Agricultural Chemistry, Faculty of Agriculture, Saba Basha, Alexandria University, Egypt  \nCorresponding author:  \nNicholas J Watson, University of Nottingham, University Park, Nottingham, NG7 2RD, UK.  \nEmail: [nicholas.watson@nottingham.ac.uk](nicholas.watson@nottingham.ac.uk) ","cbCaitdD6NCjVRID","https://ap.wps.com/l/cbCaitdD6NCjVRID","pdf",1364186,1,18,"English","en",105,"# Introduction\n## Background and grading standards\n## Limitations of manual inspection and HVI\n# Proposed Solution\n## Image capture and preprocessing\n## Feature extraction and trash removal\n# Machine Learning Models\n## Supervised algorithms and evaluation\n## Accuracy results and error sources\n## Unsupervised methods for label-error detection","[{\"question\":\"Why is manual Egyptian cotton lint grading considered problematic?\",\"answer\":\"Manual grading requires significant labor, has low inspection efficiency, and is influenced by inspection conditions such as light and human subjectivity, reducing consistency.\"},{\"question\":\"How does the proposed method adapt machine learning for Egyptian cotton lint?\",\"answer\":\"It uses image processing to remove trash effects on color measurements and extracts features that capture intra-sample variance, reflecting Egyptian cotton’s unique characteristics.\"},{\"question\":\"Which machine learning model performed best and what limited accuracy?\",\"answer\":\"Random forest produced the highest accuracy models (82.13–90.21%). Accuracy was limited by human error in the labeling used to train the classifiers.\"}]","An Image Processing and Machine Learning Solution to Automate Egyptian Cotton Lint Grading | PDF",1785818874,45,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"an-image-processing-and-machine-learning-solution-to-automate-egyptian-cotton-lint-grading","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/an-image-processing-and-machine-learning-solution-to-automate-egyptian-cotton-lint-grading/123849/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is manual Egyptian cotton lint grading considered problematic?","Question",{"text":75,"@type":76},"Manual grading requires significant labor, has low inspection efficiency, and is influenced by inspection conditions such as light and human subjectivity, reducing consistency.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed method adapt machine learning for Egyptian cotton lint?",{"text":80,"@type":76},"It uses image processing to remove trash effects on color measurements and extracts features that capture intra-sample variance, reflecting Egyptian cotton’s unique characteristics.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning model performed best and what limited accuracy?",{"text":84,"@type":76},"Random forest produced the highest accuracy models (82.13–90.21%). 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