Data and code from: High-throughput UAV phenomics and machine learning enables accurate early single-plant biomass prediction in lettuce

Adak, Alper

DOI: 10.5061/dryad.73n5tb3d4
Published: 2026
Container: Not recorded
Publisher: Dryad
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.5061/dryad.73n5tb3d4,
  title = {Data and code from: High-throughput UAV phenomics and machine learning enables accurate early single-plant biomass prediction in lettuce},
  author = {Adak, Alper},
  year = {2026},
  doi = {10.5061/dryad.73n5tb3d4},
  url = {https://doi.org/10.5061/dryad.73n5tb3d4}
}

RIS

TY  - JOUR
TI  - Data and code from: High-throughput UAV phenomics and machine learning enables accurate early single-plant biomass prediction in lettuce
AU  - Adak, Alper
PY  - 2026
DO  - 10.5061/dryad.73n5tb3d4
UR  - https://doi.org/10.5061/dryad.73n5tb3d4
ER  -

APA

Alper, A. (2026). Data and code from: High-throughput UAV phenomics and machine learning enables accurate early single-plant biomass prediction in lettuce. https://doi.org/10.5061/dryad.73n5tb3d4

Source records

datacite · retrieved 2026-11-09T12:58:56.752Z