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Journal Article

Image-based automated recognition of 31 Poaceae species: The most relevant perspectives

MPS-Authors
/persons/resource/persons186269

Rzanny,  Michael
Flora Incognita, Dr. Jana Wäldchen, Department Biogeochemical Integration, Prof. Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;

/persons/resource/persons220173

Deggelmann,  Alice
Flora Incognita, Dr. Jana Wäldchen, Department Biogeochemical Integration, Prof. Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;

/persons/resource/persons62597

Wäldchen,  Jana
Flora Incognita, Dr. Jana Wäldchen, Department Biogeochemical Integration, Prof. Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;

External Resource

https://doi.org/10.3389/fpls.2021.804140
(Publisher version)

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BGC3807.pdf
(Publisher version), 6MB

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Citation

Rzanny, M., Wittich, H. C., Mäder, P., Deggelmann, A., Boho, D., & Wäldchen, J. (2022). Image-based automated recognition of 31 Poaceae species: The most relevant perspectives. Frontiers in Plant Science, 12: 804140. doi:10.3389/fpls.2021.804140.


Cite as: https://hdl.handle.net/21.11116/0000-0009-E34D-1
Abstract
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