Weed Detection from Unmanned Aerial Vehicle Imagery Using Deep Learning—A Comparison between High-End and Low-Cost Multispectral Sensors

Anna Teresa Seiche, Lucas Wittstruck, Thomas Jarmer

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DOI: 10.3390/s24051544
Published: 2024-02-28
Container: Sensors
Publisher: MDPI AG
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.3390/s24051544,
  title = {Weed Detection from Unmanned Aerial Vehicle Imagery Using Deep Learning—A Comparison between High-End and Low-Cost Multispectral Sensors},
  author = {Anna Teresa Seiche and Lucas Wittstruck and Thomas Jarmer},
  year = {2024},
  journal = {Sensors},
  doi = {10.3390/s24051544},
  url = {https://doi.org/10.3390/s24051544}
}

RIS

TY  - JOUR
TI  - Weed Detection from Unmanned Aerial Vehicle Imagery Using Deep Learning—A Comparison between High-End and Low-Cost Multispectral Sensors
AU  - Anna Teresa Seiche
AU  - Lucas Wittstruck
AU  - Thomas Jarmer
PY  - 2024
JO  - Sensors
DO  - 10.3390/s24051544
UR  - https://doi.org/10.3390/s24051544
ER  -

APA

Seiche, A. T., Wittstruck, L., & Jarmer, T. (2024). Weed Detection from Unmanned Aerial Vehicle Imagery Using Deep Learning—A Comparison between High-End and Low-Cost Multispectral Sensors. Sensors. https://doi.org/10.3390/s24051544

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crossref · retrieved 2026-11-10T07:36:05.877Z