As good as human experts in detecting plant roots in minirhizotron images but efficient and reproducible: the convolutional neural network “RootDetector”
- DOI
- 10.1038/s41598-023-28400-x
- Published
- 2023-01-25
- Container
- Scientific Reports
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1038/s41598-023-28400-x,
title = {As good as human experts in detecting plant roots in minirhizotron images but efficient and reproducible: the convolutional neural network “RootDetector”},
author = {Bo Peters and Gesche Blume-Werry and Alexander Gillert and Sarah Schwieger and Uwe Freiherr von Lukas and Juergen Kreyling},
year = {2023},
journal = {Scientific Reports},
doi = {10.1038/s41598-023-28400-x},
url = {https://doi.org/10.1038/s41598-023-28400-x}
}RIS
TY - JOUR TI - As good as human experts in detecting plant roots in minirhizotron images but efficient and reproducible: the convolutional neural network “RootDetector” AU - Bo Peters AU - Gesche Blume-Werry AU - Alexander Gillert AU - Sarah Schwieger AU - Uwe Freiherr von Lukas AU - Juergen Kreyling PY - 2023 JO - Scientific Reports DO - 10.1038/s41598-023-28400-x UR - https://doi.org/10.1038/s41598-023-28400-x ER -
APA
Peters, B., Blume-Werry, G., Gillert, A., Schwieger, S., Lukas, U. F. V., & Kreyling, J. (2023). As good as human experts in detecting plant roots in minirhizotron images but efficient and reproducible: the convolutional neural network “RootDetector”. Scientific Reports. https://doi.org/10.1038/s41598-023-28400-x
Source records
- crossref · retrieved 2026-11-07T05:21:16.509Z