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Uses of selection strategies in both spectral and sample spaces for classifying hard and soft blueberry using near infrared data
In the current work, we attempt to leverage the fewer wavelengths and samples to develop a classification model for classifying hard and soft blueberries using near infrared (NIR) data. To do this, random frog selection and active learning approaches are used in the spectral space and the sample que...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5923227/ https://www.ncbi.nlm.nih.gov/pubmed/29703949 http://dx.doi.org/10.1038/s41598-018-25055-x |
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author | Hu, Menghan Zhai, Guangtao Zhao, Yu Wang, Zhaodi |
author_facet | Hu, Menghan Zhai, Guangtao Zhao, Yu Wang, Zhaodi |
author_sort | Hu, Menghan |
collection | PubMed |
description | In the current work, we attempt to leverage the fewer wavelengths and samples to develop a classification model for classifying hard and soft blueberries using near infrared (NIR) data. To do this, random frog selection and active learning approaches are used in the spectral space and the sample queue, respectively. To reduce the spectral number, a random frog spectral selection approach was applied to collect wavelengths informative with hardness. Prediction model based on 22 selected spectra gave slightly better results than that based on the full spectra. In terms of the selection operation in the sample space, the query by committee was validated to be suitable for blueberry hardness classification with the accuracy, precision and recall of 78%, 74% and 98% when taking only 25 sample queries. Its standard deviation curves of performance metrics are also located in regions of low values (around 0.05) and fluctuated steadily in shape, winning over those of the other 4 active learning strategies and random method. In summary, the respective uses of random frog and query by committee in the NIR spectral vector and the sample queue showed the considerable potential for establishing a simple but robust classifier for hard and soft blueberries with very low labeling cost. |
format | Online Article Text |
id | pubmed-5923227 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-59232272018-05-01 Uses of selection strategies in both spectral and sample spaces for classifying hard and soft blueberry using near infrared data Hu, Menghan Zhai, Guangtao Zhao, Yu Wang, Zhaodi Sci Rep Article In the current work, we attempt to leverage the fewer wavelengths and samples to develop a classification model for classifying hard and soft blueberries using near infrared (NIR) data. To do this, random frog selection and active learning approaches are used in the spectral space and the sample queue, respectively. To reduce the spectral number, a random frog spectral selection approach was applied to collect wavelengths informative with hardness. Prediction model based on 22 selected spectra gave slightly better results than that based on the full spectra. In terms of the selection operation in the sample space, the query by committee was validated to be suitable for blueberry hardness classification with the accuracy, precision and recall of 78%, 74% and 98% when taking only 25 sample queries. Its standard deviation curves of performance metrics are also located in regions of low values (around 0.05) and fluctuated steadily in shape, winning over those of the other 4 active learning strategies and random method. In summary, the respective uses of random frog and query by committee in the NIR spectral vector and the sample queue showed the considerable potential for establishing a simple but robust classifier for hard and soft blueberries with very low labeling cost. Nature Publishing Group UK 2018-04-27 /pmc/articles/PMC5923227/ /pubmed/29703949 http://dx.doi.org/10.1038/s41598-018-25055-x Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Hu, Menghan Zhai, Guangtao Zhao, Yu Wang, Zhaodi Uses of selection strategies in both spectral and sample spaces for classifying hard and soft blueberry using near infrared data |
title | Uses of selection strategies in both spectral and sample spaces for classifying hard and soft blueberry using near infrared data |
title_full | Uses of selection strategies in both spectral and sample spaces for classifying hard and soft blueberry using near infrared data |
title_fullStr | Uses of selection strategies in both spectral and sample spaces for classifying hard and soft blueberry using near infrared data |
title_full_unstemmed | Uses of selection strategies in both spectral and sample spaces for classifying hard and soft blueberry using near infrared data |
title_short | Uses of selection strategies in both spectral and sample spaces for classifying hard and soft blueberry using near infrared data |
title_sort | uses of selection strategies in both spectral and sample spaces for classifying hard and soft blueberry using near infrared data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5923227/ https://www.ncbi.nlm.nih.gov/pubmed/29703949 http://dx.doi.org/10.1038/s41598-018-25055-x |
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