Automated collection of single species of microfossils using a deep learning–micromanipulator system
Abstract For geochemical analysis such as stable isotope ratio, radiocarbon dating and minor element analysis for a single species of microfossils, a large number of specimens, is required. Collecting specimens one by one under a microscope requires enormous time and effort. In this study, we develo...
| मुख्य लेखकों: | , , , , , |
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| स्वरूप: | लेख |
| भाषा: | English |
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SpringerOpen
2020-05-01
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| श्रृंखला: | Progress in Earth and Planetary Science |
| विषय: | |
| ऑनलाइन पहुंच: | http://link.springer.com/article/10.1186/s40645-020-00332-4 |
| _version_ | 1828836515248078848 |
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| author | Takuya Itaki Yosuke Taira Naoki Kuwamori Toshinori Maebayashi Satoshi Takeshima Kenji Toya |
| author_facet | Takuya Itaki Yosuke Taira Naoki Kuwamori Toshinori Maebayashi Satoshi Takeshima Kenji Toya |
| author_sort | Takuya Itaki |
| collection | DOAJ |
| description | Abstract For geochemical analysis such as stable isotope ratio, radiocarbon dating and minor element analysis for a single species of microfossils, a large number of specimens, is required. Collecting specimens one by one under a microscope requires enormous time and effort. In this study, we developed a device that automates these efforts and can be used without expert knowledge. Microfossils can be accurately classified and identified to taxonomic species level using deep learning, which is one of the learning methods of artificial intelligence (AI), and picked up using a micromanipulator installed in the microscope with an automated motorized X-Y stage. A prototype of the classification model AI-PIC_20181024 showed the ability to classify microfossil species Cycladophora davisiana and Actinomma boreale (radiolarians) with accuracy exceeding 90% at a confidence level > 0.90. Using this method, it is possible to collect a large number of particles with speed and accuracy that cannot be achieved by a human technician. Although this technology can only be used for specific species of microfossils, it greatly reduces the hand work of picking and also enables chemical analysis, such as isotope ratio and minor element analysis, for small microfossil species for which it had been difficult to collect enough specimens. In addition to microfossils, this technology can be applied to other particles, with applications expected in various fields, such as medical, food, horticulture, and materials. |
| first_indexed | 2024-12-12T18:22:48Z |
| format | Article |
| id | doaj.art-41e04b8e71c2480c8627f81d5d16a8f9 |
| institution | Directory Open Access Journal |
| issn | 2197-4284 |
| language | English |
| last_indexed | 2024-12-12T18:22:48Z |
| publishDate | 2020-05-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Progress in Earth and Planetary Science |
| spelling | doaj.art-41e04b8e71c2480c8627f81d5d16a8f92022-12-22T00:16:06ZengSpringerOpenProgress in Earth and Planetary Science2197-42842020-05-01711710.1186/s40645-020-00332-4Automated collection of single species of microfossils using a deep learning–micromanipulator systemTakuya Itaki0Yosuke Taira1Naoki Kuwamori2Toshinori Maebayashi3Satoshi Takeshima4Kenji Toya5Geological Survey of Japan/AIST, Institute of Geology and Geoinformation1st Government and Public Solutions Division, NEC Corporation1st Government and Public Solutions Division, NEC CorporationHead Office Sales Department, Micro Support Co., Ltd.Visual System Division, Mitani CorporationVisual System Division, Mitani CorporationAbstract For geochemical analysis such as stable isotope ratio, radiocarbon dating and minor element analysis for a single species of microfossils, a large number of specimens, is required. Collecting specimens one by one under a microscope requires enormous time and effort. In this study, we developed a device that automates these efforts and can be used without expert knowledge. Microfossils can be accurately classified and identified to taxonomic species level using deep learning, which is one of the learning methods of artificial intelligence (AI), and picked up using a micromanipulator installed in the microscope with an automated motorized X-Y stage. A prototype of the classification model AI-PIC_20181024 showed the ability to classify microfossil species Cycladophora davisiana and Actinomma boreale (radiolarians) with accuracy exceeding 90% at a confidence level > 0.90. Using this method, it is possible to collect a large number of particles with speed and accuracy that cannot be achieved by a human technician. Although this technology can only be used for specific species of microfossils, it greatly reduces the hand work of picking and also enables chemical analysis, such as isotope ratio and minor element analysis, for small microfossil species for which it had been difficult to collect enough specimens. In addition to microfossils, this technology can be applied to other particles, with applications expected in various fields, such as medical, food, horticulture, and materials.http://link.springer.com/article/10.1186/s40645-020-00332-4MicropaleontologyRadiolariansAIClassificationManipulation |
| spellingShingle | Takuya Itaki Yosuke Taira Naoki Kuwamori Toshinori Maebayashi Satoshi Takeshima Kenji Toya Automated collection of single species of microfossils using a deep learning–micromanipulator system Progress in Earth and Planetary Science Micropaleontology Radiolarians AI Classification Manipulation |
| title | Automated collection of single species of microfossils using a deep learning–micromanipulator system |
| title_full | Automated collection of single species of microfossils using a deep learning–micromanipulator system |
| title_fullStr | Automated collection of single species of microfossils using a deep learning–micromanipulator system |
| title_full_unstemmed | Automated collection of single species of microfossils using a deep learning–micromanipulator system |
| title_short | Automated collection of single species of microfossils using a deep learning–micromanipulator system |
| title_sort | automated collection of single species of microfossils using a deep learning micromanipulator system |
| topic | Micropaleontology Radiolarians AI Classification Manipulation |
| url | http://link.springer.com/article/10.1186/s40645-020-00332-4 |
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