An open-source machine-learning approach for obtaining high-quality quantitative wood anatomy data from E. grandis and P. radiata xylem
Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.
| Veröffentlicht in: | Plant science : an international journal of experimental plant biology. - 1985. - 340(2024) vom: 25. März, Seite 111970 |
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| Weitere Verfasser: | , , , , , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2024
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| Zugriff auf das übergeordnete Werk: | Plant science : an international journal of experimental plant biology |
| Schlagworte: | Journal Article Cell-detection Hardwood Histology Machine learning Softwood Xylogenesis |
| Zusammenfassung: | Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved. Quantitative wood anatomy is a subfield in dendrochronology that requires effective open-source image analysis tools. In this research, the bioimage analysis software QuPath (v0.4.4) is introduced as a candidate for accurately quantifying the cellular properties of the xylem in an automated manner. Additionally, the potential of QuPath to detect the transition of early- to latewood tracheids over the growing season was evaluated to assess a potential application in dendroecological studies. Various algorithms in QuPath were optimized to quantify different xylem cell types in Eucalyptus grandis and the transition of early- to latewood tracheids in Pinus radiata. These algorithms were coded into cell detection scripts for automatic quantification of stem microsections and compared to a manually curated method to assess the accuracy of the cell detections. The automatic cell detection approach, using QuPath, has been validated to be reproducible with an acceptable error when assessing fibers, vessels, early- and latewood tracheids. However, further optimization for parenchyma is still required. This proposed method developed in QuPath provides a scalable and accurate approach for quantifying anatomical features in stem microsections. With minor amendments to the detection and classification algorithms, this strategy is likely to be viable in other plant species |
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| Beschreibung: | Date Completed 05.02.2024 Date Revised 08.02.2024 published: Print-Electronic Citation Status MEDLINE |
| ISSN: | 1873-2259 |
| DOI: | 10.1016/j.plantsci.2023.111970 |