Fisher, O.J., Rady, A., El-Banna, A.A.A. et al. (2 more authors) (2023) AI-Assisted Cotton Grading: Active and Semi-Supervised Learning to Reduce the Image-Labelling Burden. Sensors, 23 (21). 8671. ISSN 1424-8220
Abstract
The assessment of food and industrial crops during harvesting is important to determine the quality and downstream processing requirements, which in turn affect their market value. While machine learning models have been developed for this purpose, their deployment is hindered by the high cost of labelling the crop images to provide data for model training. This study examines the capabilities of semi-supervised and active learning to minimise effort when labelling cotton lint samples while maintaining high classification accuracy. Random forest classification models were developed using supervised learning, semi-supervised learning, and active learning to determine Egyptian cotton grade. Compared to supervised learning (80.20-82.66%) and semi-supervised learning (81.39-85.26%), active learning models were able to achieve higher accuracy (82.85-85.33%) with up to 46.4% reduction in the volume of labelled data required. The primary obstacle when using machine learning for Egyptian cotton grading is the time required for labelling cotton lint samples. However, by applying active learning, this study successfully decreased the time needed from 422.5 to 177.5 min. The findings of this study demonstrate that active learning is a promising approach for developing accurate and efficient machine learning models for grading food and industrial crops.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 by the authors. This is an open access article under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. |
Keywords: | machine learning; digital manufacturing; cotton; colour vision system; quality assessment; semi-supervised learning; active learning |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > School of Food Science and Nutrition (Leeds) > FSN Nutrition and Public Health (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 10 Jul 2024 15:55 |
Last Modified: | 10 Jul 2024 15:55 |
Status: | Published |
Publisher: | MDPI |
Identification Number: | 10.3390/s23218671 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:214602 |