Plant Phenotyping using Probabilistic Topic Models: Uncovering the Hyperspectral Language of Plants.

Wahabzada M, Mahlein AK, Bauckhage C, Steiner U, Oerke EC, Kersting K

DOI: 10.1038/srep22482
Published: 2016 Mar 9
Container: Scientific reports
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.1038/srep22482,
  title = {Plant Phenotyping using Probabilistic Topic Models: Uncovering the Hyperspectral Language of Plants.},
  author = {Wahabzada M and Mahlein AK and Bauckhage C and Steiner U and Oerke EC and Kersting K},
  year = {2016},
  journal = {Scientific reports},
  doi = {10.1038/srep22482},
  url = {https://doi.org/10.1038/srep22482}
}

RIS

TY  - JOUR
TI  - Plant Phenotyping using Probabilistic Topic Models: Uncovering the Hyperspectral Language of Plants.
AU  - Wahabzada M
AU  - Mahlein AK
AU  - Bauckhage C
AU  - Steiner U
AU  - Oerke EC
AU  - Kersting K
PY  - 2016
JO  - Scientific reports
DO  - 10.1038/srep22482
UR  - https://doi.org/10.1038/srep22482
ER  -

APA

M, W., AK, M., C, B., U, S., EC, O., & K, K. (2016). Plant Phenotyping using Probabilistic Topic Models: Uncovering the Hyperspectral Language of Plants.. Scientific reports. https://doi.org/10.1038/srep22482

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pubmed · retrieved 2026-09-26T00:04:26.154Z