{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Fisher OJ"],"funding":["Engineering and Physical Sciences Research Council"],"pagination":["8671"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10647751"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["23(21)"],"pubmed_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 lea"],"journal":["Sensors (Basel, Switzerland)"],"pubmed_title":["AI-Assisted Cotton Grading: Active and Semi-Supervised Learning to Reduce the Image-Labelling Burden."],"pmcid":["PMC10647751"],"funding_grant_id":["EP/S036113/1"],"pubmed_authors":["Emaish HH","Rady A","Watson NJ","El-Banna AAA","Fisher OJ"],"additional_accession":[]},"is_claimable":false,"name":"AI-Assisted Cotton Grading: Active and Semi-Supervised Learning to Reduce the Image-Labelling Burden.","description":"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 lea","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Oct","modification":"2025-04-26T17:18:26.109Z","creation":"2025-04-06T15:28:23.569Z"},"accession":"S-EPMC10647751","cross_references":{"pubmed":["37960371"],"doi":["10.3390/s23218671"]}}