UAV-based estimation of sunflower leaf flavonol content across multiple flight heights using RGB-multispectral canopy features and machine learning.
- DOI
- 10.3389/fpls.2026.1931472
- Published
- 2026
- Container
- Frontiers in plant science
- Publisher
- Not recorded
- Open access
- yes
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limited evidence Score 45/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
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Cite this work
BibTeX
@article{allodium:10.3389/fpls.2026.1931472,
title = {UAV-based estimation of sunflower leaf flavonol content across multiple flight heights using RGB-multispectral canopy features and machine learning.},
author = {Han G and Zhao J and Song Z and Zhang Z and Lu L and Yuan J},
year = {2026},
journal = {Frontiers in plant science},
doi = {10.3389/fpls.2026.1931472},
url = {https://doi.org/10.3389/fpls.2026.1931472}
}RIS
TY - JOUR TI - UAV-based estimation of sunflower leaf flavonol content across multiple flight heights using RGB-multispectral canopy features and machine learning. AU - Han G AU - Zhao J AU - Song Z AU - Zhang Z AU - Lu L AU - Yuan J PY - 2026 JO - Frontiers in plant science DO - 10.3389/fpls.2026.1931472 UR - https://doi.org/10.3389/fpls.2026.1931472 ER -
APA
G, H., J, Z., Z, S., Z, Z., L, L., & J, Y. (2026). UAV-based estimation of sunflower leaf flavonol content across multiple flight heights using RGB-multispectral canopy features and machine learning.. Frontiers in plant science. https://doi.org/10.3389/fpls.2026.1931472
Source records
- pubmed · retrieved 2026-09-25T06:43:09.146Z