As good as human experts in detecting plant roots in minirhizotron images but efficient and reproducible: the convolutional neural network "RootDetector".

Peters B, Blume-Werry G, Gillert A, Schwieger S, von Lukas UF, Kreyling J

Open source

DOI
10.1038/s41598-023-28400-x
Published
2023 Jan 25
Container
Scientific reports
Publisher
Not recorded
Open access
yes

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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

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