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  Semantic segmentation of plant roots from RGB (mini-) rhizotron images—generalisation potential and false positives of established methods and advanced deep-learning models

Baykalov, P., Bussmann, B., Nair, R., Smith, A. G., Bodner, G., Hadar, O., et al. (2023). Semantic segmentation of plant roots from RGB (mini-) rhizotron images—generalisation potential and false positives of established methods and advanced deep-learning models. Plant Methods, 19: 122. doi:10.1186/s13007-023-01101-2.

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Item Permalink: https://hdl.handle.net/21.11116/0000-000D-E173-3 Version Permalink: https://hdl.handle.net/21.11116/0000-000D-E174-2
Genre: Journal Article

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https://doi.org/10.1186/s13007-023-01101-2 (Publisher version)
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 Creators:
Baykalov, Pavel, Author
Bussmann, Bart, Author
Nair, Richard1, Author           
Smith, Abraham George, Author
Bodner, Gernot, Author
Hadar, Ofer, Author
Lazarovitch, Naftali, Author
Rewald, Boris, Author
Affiliations:
1Soil Processes, Dr. Marion Schrumpf, Department Biogeochemical Integration, Prof. Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society, ou_1938308              

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 Dates: 2023-10-272023-11-06
 Publication Status: Published online
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: Other: BGC4386
DOI: 10.1186/s13007-023-01101-2
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Title: Plant Methods
Source Genre: Journal
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Publ. Info: BioMed Central
Pages: - Volume / Issue: 19 Sequence Number: 122 Start / End Page: - Identifier: ISSN: 1746-4811
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000019420