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Deep Learning Algorithms Correctly Classify Brassica rapa Varieties Using Digital Images

Efficient and accurate methods of analysis are needed for the huge amount of biological data that have accumulated in various research fields, including genomics, phenomics, and genetics. Artificial intelligence (AI)-based analysis is one promising method to manipulate biological data. To this end,...

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Autores principales: Jung, Minah, Song, Jong Seob, Hong, Seongmin, Kim, SunWoo, Go, Sangjin, Lim, Yong Pyo, Park, Juhan, Park, Sung Goo, Kim, Yong-Min
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8511822/
https://www.ncbi.nlm.nih.gov/pubmed/34659305
http://dx.doi.org/10.3389/fpls.2021.738685
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author Jung, Minah
Song, Jong Seob
Hong, Seongmin
Kim, SunWoo
Go, Sangjin
Lim, Yong Pyo
Park, Juhan
Park, Sung Goo
Kim, Yong-Min
author_facet Jung, Minah
Song, Jong Seob
Hong, Seongmin
Kim, SunWoo
Go, Sangjin
Lim, Yong Pyo
Park, Juhan
Park, Sung Goo
Kim, Yong-Min
author_sort Jung, Minah
collection PubMed
description Efficient and accurate methods of analysis are needed for the huge amount of biological data that have accumulated in various research fields, including genomics, phenomics, and genetics. Artificial intelligence (AI)-based analysis is one promising method to manipulate biological data. To this end, various algorithms have been developed and applied in fields such as disease diagnosis, species classification, and object prediction. In the field of phenomics, classification of accessions and variants is important for basic science and industrial applications. To construct AI-based classification models, three types of phenotypic image data were generated from 156 Brassica rapa core collections, and classification analyses were carried out using four different convolutional neural network architectures. The results of lateral view data showed higher accuracy compared with top view data. Furthermore, the relatively low accuracy of ResNet50 architecture suggested that definition and estimation of similarity index of phenotypic data were required before the selection of deep learning architectures.
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spelling pubmed-85118222021-10-14 Deep Learning Algorithms Correctly Classify Brassica rapa Varieties Using Digital Images Jung, Minah Song, Jong Seob Hong, Seongmin Kim, SunWoo Go, Sangjin Lim, Yong Pyo Park, Juhan Park, Sung Goo Kim, Yong-Min Front Plant Sci Plant Science Efficient and accurate methods of analysis are needed for the huge amount of biological data that have accumulated in various research fields, including genomics, phenomics, and genetics. Artificial intelligence (AI)-based analysis is one promising method to manipulate biological data. To this end, various algorithms have been developed and applied in fields such as disease diagnosis, species classification, and object prediction. In the field of phenomics, classification of accessions and variants is important for basic science and industrial applications. To construct AI-based classification models, three types of phenotypic image data were generated from 156 Brassica rapa core collections, and classification analyses were carried out using four different convolutional neural network architectures. The results of lateral view data showed higher accuracy compared with top view data. Furthermore, the relatively low accuracy of ResNet50 architecture suggested that definition and estimation of similarity index of phenotypic data were required before the selection of deep learning architectures. Frontiers Media S.A. 2021-09-29 /pmc/articles/PMC8511822/ /pubmed/34659305 http://dx.doi.org/10.3389/fpls.2021.738685 Text en Copyright © 2021 Jung, Song, Hong, Kim, Go, Lim, Park, Park and Kim. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
Jung, Minah
Song, Jong Seob
Hong, Seongmin
Kim, SunWoo
Go, Sangjin
Lim, Yong Pyo
Park, Juhan
Park, Sung Goo
Kim, Yong-Min
Deep Learning Algorithms Correctly Classify Brassica rapa Varieties Using Digital Images
title Deep Learning Algorithms Correctly Classify Brassica rapa Varieties Using Digital Images
title_full Deep Learning Algorithms Correctly Classify Brassica rapa Varieties Using Digital Images
title_fullStr Deep Learning Algorithms Correctly Classify Brassica rapa Varieties Using Digital Images
title_full_unstemmed Deep Learning Algorithms Correctly Classify Brassica rapa Varieties Using Digital Images
title_short Deep Learning Algorithms Correctly Classify Brassica rapa Varieties Using Digital Images
title_sort deep learning algorithms correctly classify brassica rapa varieties using digital images
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8511822/
https://www.ncbi.nlm.nih.gov/pubmed/34659305
http://dx.doi.org/10.3389/fpls.2021.738685
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