Innovative microfossil (radiolarian) analysis using a system for automated image collection and AI-based classification of species
Abstract Microfossils are a powerful tool in earth sciences, and they have been widely used for the determination of geological age and in paleoenvironmental studies. However, the identification of fossil species requires considerable time and labor by experts with extensive knowledge and experience...
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2020
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Online Access: | http://dx.doi.org/10.1038/s41598-020-77812-6 http://www.nature.com/articles/s41598-020-77812-6.pdf http://www.nature.com/articles/s41598-020-77812-6 |
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crspringernat:10.1038/s41598-020-77812-6 2023-05-15T18:25:21+02:00 Innovative microfossil (radiolarian) analysis using a system for automated image collection and AI-based classification of species Itaki, Takuya Taira, Yosuke Kuwamori, Naoki Saito, Hitoshi Ikehara, Minoru Hoshino, Tatsuhiko JSPS KAKENHI 2020 http://dx.doi.org/10.1038/s41598-020-77812-6 http://www.nature.com/articles/s41598-020-77812-6.pdf http://www.nature.com/articles/s41598-020-77812-6 en eng Springer Science and Business Media LLC https://creativecommons.org/licenses/by/4.0 https://creativecommons.org/licenses/by/4.0 CC-BY Scientific Reports volume 10, issue 1 ISSN 2045-2322 Multidisciplinary journal-article 2020 crspringernat https://doi.org/10.1038/s41598-020-77812-6 2022-01-04T07:41:58Z Abstract Microfossils are a powerful tool in earth sciences, and they have been widely used for the determination of geological age and in paleoenvironmental studies. However, the identification of fossil species requires considerable time and labor by experts with extensive knowledge and experience. In this study, we successfully automated the acquisition of microfossil data using an artificial intelligence system that employs a computer-controlled microscope and deep learning methods. The system was used to calculate changes in the relative abundance (%) of Cycladophora davisiana , a siliceous microfossil species (Radiolaria) that is widely used as a stratigraphic tool in studies on Pleistocene sediments in the Southern Ocean. The estimates obtained using this system were consistent with the results obtained by a human expert (< ± 3.2%). In terms of efficiency, the developed system was capable of performing the classification tasks approximately three times faster than a human expert performing the same task. Article in Journal/Newspaper Southern Ocean Springer Nature (via Crossref) Southern Ocean Scientific Reports 10 1 |
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English |
topic |
Multidisciplinary |
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Multidisciplinary Itaki, Takuya Taira, Yosuke Kuwamori, Naoki Saito, Hitoshi Ikehara, Minoru Hoshino, Tatsuhiko Innovative microfossil (radiolarian) analysis using a system for automated image collection and AI-based classification of species |
topic_facet |
Multidisciplinary |
description |
Abstract Microfossils are a powerful tool in earth sciences, and they have been widely used for the determination of geological age and in paleoenvironmental studies. However, the identification of fossil species requires considerable time and labor by experts with extensive knowledge and experience. In this study, we successfully automated the acquisition of microfossil data using an artificial intelligence system that employs a computer-controlled microscope and deep learning methods. The system was used to calculate changes in the relative abundance (%) of Cycladophora davisiana , a siliceous microfossil species (Radiolaria) that is widely used as a stratigraphic tool in studies on Pleistocene sediments in the Southern Ocean. The estimates obtained using this system were consistent with the results obtained by a human expert (< ± 3.2%). In terms of efficiency, the developed system was capable of performing the classification tasks approximately three times faster than a human expert performing the same task. |
author2 |
JSPS KAKENHI |
format |
Article in Journal/Newspaper |
author |
Itaki, Takuya Taira, Yosuke Kuwamori, Naoki Saito, Hitoshi Ikehara, Minoru Hoshino, Tatsuhiko |
author_facet |
Itaki, Takuya Taira, Yosuke Kuwamori, Naoki Saito, Hitoshi Ikehara, Minoru Hoshino, Tatsuhiko |
author_sort |
Itaki, Takuya |
title |
Innovative microfossil (radiolarian) analysis using a system for automated image collection and AI-based classification of species |
title_short |
Innovative microfossil (radiolarian) analysis using a system for automated image collection and AI-based classification of species |
title_full |
Innovative microfossil (radiolarian) analysis using a system for automated image collection and AI-based classification of species |
title_fullStr |
Innovative microfossil (radiolarian) analysis using a system for automated image collection and AI-based classification of species |
title_full_unstemmed |
Innovative microfossil (radiolarian) analysis using a system for automated image collection and AI-based classification of species |
title_sort |
innovative microfossil (radiolarian) analysis using a system for automated image collection and ai-based classification of species |
publisher |
Springer Science and Business Media LLC |
publishDate |
2020 |
url |
http://dx.doi.org/10.1038/s41598-020-77812-6 http://www.nature.com/articles/s41598-020-77812-6.pdf http://www.nature.com/articles/s41598-020-77812-6 |
geographic |
Southern Ocean |
geographic_facet |
Southern Ocean |
genre |
Southern Ocean |
genre_facet |
Southern Ocean |
op_source |
Scientific Reports volume 10, issue 1 ISSN 2045-2322 |
op_rights |
https://creativecommons.org/licenses/by/4.0 https://creativecommons.org/licenses/by/4.0 |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.1038/s41598-020-77812-6 |
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Scientific Reports |
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10 |
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1 |
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1766206756018454528 |