Computer Vision and Transfer Learning for Grading of Egyptian Cotton Fibres
Egyptian cotton fibres have worldwide recognition due to their distinct quality and luxurious textile products known by the “Egyptian Cotton“ label. However, cotton fibre trading in Egypt still depends on human grading of cotton quality, which is resource-intensive and faces challenges in terms of s...
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MDPI AG
2025-04-01
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| Online Access: | https://www.mdpi.com/2624-7402/7/5/127 |
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| author | Ahmed Rady Oliver Fisher Aly A. A. El-Banna Haitham H. Emasih Nicholas J. Watson |
| author_facet | Ahmed Rady Oliver Fisher Aly A. A. El-Banna Haitham H. Emasih Nicholas J. Watson |
| author_sort | Ahmed Rady |
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| description | Egyptian cotton fibres have worldwide recognition due to their distinct quality and luxurious textile products known by the “Egyptian Cotton“ label. However, cotton fibre trading in Egypt still depends on human grading of cotton quality, which is resource-intensive and faces challenges in terms of subjectivity and expertise requirements. This study investigates colour vision and transfer learning to classify the grade of five long (Giza 86, Giza 90, and Giza 94) and extra-long (Giza 87 and Giza 96) staple cotton cultivars. Five Convolutional Neural networks (CNNs)—AlexNet, GoogleNet, SqueezeNet, VGG16, and VGG19—were fine-tuned, optimised, and tested on independent datasets. The highest classifications were 75.7%, 85.0%, 80.0%, 77.1%, and 90.0% for Giza 86, Giza 87, Giza 90, Giza 94, and Giza 96, respectively, with F1-Scores ranging from 51.9–100%, 66.7–100%, 42.9–100%, 40.0–100%, and 80.0–100%. Among the CNNs, AlexNet, GoogleNet, and VGG19 outperformed the others. Fused CNN models further improved classification accuracy by up to 7.2% for all cultivars except Giza 87. These results demonstrate the feasibility of developing a fast, low-cost, and low-skilled vision system that overcomes the inconsistencies and limitations of manual grading in the early stages of cotton fibre trading in Egypt. |
| format | Article |
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| institution | Kabale University |
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| language | English |
| publishDate | 2025-04-01 |
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| spelling | doaj-art-51b43e7357e948599b335ebb2d716f152025-08-20T03:47:52ZengMDPI AGAgriEngineering2624-74022025-04-017512710.3390/agriengineering7050127Computer Vision and Transfer Learning for Grading of Egyptian Cotton FibresAhmed Rady0Oliver Fisher1Aly A. A. El-Banna2Haitham H. Emasih3Nicholas J. Watson4Teagasc Food Research Centre, Ashtown, D15 KN3K Dublin, IrelandFood, Water, Waste Research Group, Faculty of Engineering, University of Nottingham, Nottingham NG7 2RD, UKDepartment of Plant Production, Faculty of Agriculture, Saba Basha, Alexandria University, Alexandria 21613, EgyptDepartment of Soils and Agricultural Chemistry, Faculty of Agriculture, Saba Basha, Alexandria University, Alexandria 21613, EgyptSchool of Food Science and Nutrition, University of Leeds, Leeds LS2 9LU, UKEgyptian cotton fibres have worldwide recognition due to their distinct quality and luxurious textile products known by the “Egyptian Cotton“ label. However, cotton fibre trading in Egypt still depends on human grading of cotton quality, which is resource-intensive and faces challenges in terms of subjectivity and expertise requirements. This study investigates colour vision and transfer learning to classify the grade of five long (Giza 86, Giza 90, and Giza 94) and extra-long (Giza 87 and Giza 96) staple cotton cultivars. Five Convolutional Neural networks (CNNs)—AlexNet, GoogleNet, SqueezeNet, VGG16, and VGG19—were fine-tuned, optimised, and tested on independent datasets. The highest classifications were 75.7%, 85.0%, 80.0%, 77.1%, and 90.0% for Giza 86, Giza 87, Giza 90, Giza 94, and Giza 96, respectively, with F1-Scores ranging from 51.9–100%, 66.7–100%, 42.9–100%, 40.0–100%, and 80.0–100%. Among the CNNs, AlexNet, GoogleNet, and VGG19 outperformed the others. Fused CNN models further improved classification accuracy by up to 7.2% for all cultivars except Giza 87. These results demonstrate the feasibility of developing a fast, low-cost, and low-skilled vision system that overcomes the inconsistencies and limitations of manual grading in the early stages of cotton fibre trading in Egypt.https://www.mdpi.com/2624-7402/7/5/127Egyptian cottontransfer learningpre-trained CNNcomputer visionAlexNetGoogleNet |
| spellingShingle | Ahmed Rady Oliver Fisher Aly A. A. El-Banna Haitham H. Emasih Nicholas J. Watson Computer Vision and Transfer Learning for Grading of Egyptian Cotton Fibres AgriEngineering Egyptian cotton transfer learning pre-trained CNN computer vision AlexNet GoogleNet |
| title | Computer Vision and Transfer Learning for Grading of Egyptian Cotton Fibres |
| title_full | Computer Vision and Transfer Learning for Grading of Egyptian Cotton Fibres |
| title_fullStr | Computer Vision and Transfer Learning for Grading of Egyptian Cotton Fibres |
| title_full_unstemmed | Computer Vision and Transfer Learning for Grading of Egyptian Cotton Fibres |
| title_short | Computer Vision and Transfer Learning for Grading of Egyptian Cotton Fibres |
| title_sort | computer vision and transfer learning for grading of egyptian cotton fibres |
| topic | Egyptian cotton transfer learning pre-trained CNN computer vision AlexNet GoogleNet |
| url | https://www.mdpi.com/2624-7402/7/5/127 |
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