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Assessment of the vigor of rice seeds by near-infrared hyperspectral imaging combined with transfer learning
Rice seed vigor plays a significant role in determining the quality and quantity of rice production. Thus, the quick and non-destructive identification of seed vigor is not only beneficial to fully obtain the state of rice seeds but also the intelligent development of agriculture by instant monitori...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society of Chemistry
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9058448/ https://www.ncbi.nlm.nih.gov/pubmed/35517156 http://dx.doi.org/10.1039/d0ra06938h |
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author | Yang, Yong Chen, Jianping He, Yong Liu, Feng Feng, Xuping Zhang, Jinnuo |
author_facet | Yang, Yong Chen, Jianping He, Yong Liu, Feng Feng, Xuping Zhang, Jinnuo |
author_sort | Yang, Yong |
collection | PubMed |
description | Rice seed vigor plays a significant role in determining the quality and quantity of rice production. Thus, the quick and non-destructive identification of seed vigor is not only beneficial to fully obtain the state of rice seeds but also the intelligent development of agriculture by instant monitoring. Thus, herein, near-infrared hyperspectral imaging technology, as an information acquisition tool, was introduced combined with a deep learning algorithm to identify the rice seed vigor. Both the spectral images and average spectra of the rice seeds were sent to discriminant models including deep learning models and traditional machine learning models, and the highest accuracy of vigor identification reached 99.5018% using the self-built model. The parameters of the established deep learning models were frozen to be feature extractor for transfer learning. The identification results whose highest number also reached almost 98% indicated the possibility of applying transfer learning to improve the universality of the models. Moreover, by visualizing the output of convolutional layers, the progress and mechanism of spectral image feature extraction in the established deep learning model was explored. Overall, the self-built deep learning models combined with near-infrared hyperspectral images in the determination of rice seed vigor have potential to efficiently perform this task. |
format | Online Article Text |
id | pubmed-9058448 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | The Royal Society of Chemistry |
record_format | MEDLINE/PubMed |
spelling | pubmed-90584482022-05-04 Assessment of the vigor of rice seeds by near-infrared hyperspectral imaging combined with transfer learning Yang, Yong Chen, Jianping He, Yong Liu, Feng Feng, Xuping Zhang, Jinnuo RSC Adv Chemistry Rice seed vigor plays a significant role in determining the quality and quantity of rice production. Thus, the quick and non-destructive identification of seed vigor is not only beneficial to fully obtain the state of rice seeds but also the intelligent development of agriculture by instant monitoring. Thus, herein, near-infrared hyperspectral imaging technology, as an information acquisition tool, was introduced combined with a deep learning algorithm to identify the rice seed vigor. Both the spectral images and average spectra of the rice seeds were sent to discriminant models including deep learning models and traditional machine learning models, and the highest accuracy of vigor identification reached 99.5018% using the self-built model. The parameters of the established deep learning models were frozen to be feature extractor for transfer learning. The identification results whose highest number also reached almost 98% indicated the possibility of applying transfer learning to improve the universality of the models. Moreover, by visualizing the output of convolutional layers, the progress and mechanism of spectral image feature extraction in the established deep learning model was explored. Overall, the self-built deep learning models combined with near-infrared hyperspectral images in the determination of rice seed vigor have potential to efficiently perform this task. The Royal Society of Chemistry 2020-12-15 /pmc/articles/PMC9058448/ /pubmed/35517156 http://dx.doi.org/10.1039/d0ra06938h Text en This journal is © The Royal Society of Chemistry https://creativecommons.org/licenses/by-nc/3.0/ |
spellingShingle | Chemistry Yang, Yong Chen, Jianping He, Yong Liu, Feng Feng, Xuping Zhang, Jinnuo Assessment of the vigor of rice seeds by near-infrared hyperspectral imaging combined with transfer learning |
title | Assessment of the vigor of rice seeds by near-infrared hyperspectral imaging combined with transfer learning |
title_full | Assessment of the vigor of rice seeds by near-infrared hyperspectral imaging combined with transfer learning |
title_fullStr | Assessment of the vigor of rice seeds by near-infrared hyperspectral imaging combined with transfer learning |
title_full_unstemmed | Assessment of the vigor of rice seeds by near-infrared hyperspectral imaging combined with transfer learning |
title_short | Assessment of the vigor of rice seeds by near-infrared hyperspectral imaging combined with transfer learning |
title_sort | assessment of the vigor of rice seeds by near-infrared hyperspectral imaging combined with transfer learning |
topic | Chemistry |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9058448/ https://www.ncbi.nlm.nih.gov/pubmed/35517156 http://dx.doi.org/10.1039/d0ra06938h |
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