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 Jan 25
- Container
- Scientific reports
- Publisher
- Not recorded
- Open access
- yes
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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 = {Peters B and Blume-Werry G and Gillert A and Schwieger S and von Lukas UF and Kreyling J},
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 - Peters B AU - Blume-Werry G AU - Gillert A AU - Schwieger S AU - von Lukas UF AU - Kreyling J PY - 2023 JO - Scientific reports DO - 10.1038/s41598-023-28400-x UR - https://doi.org/10.1038/s41598-023-28400-x ER -
APA
B, P., G, B., A, G., S, S., UF, V. L., & J, K. (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
- pubmed · retrieved 2026-09-25T22:58:00.106Z