Using Hyperspectral Imaging and Principal Component Analysis to Detect and Monitor Water Stress in Ornamental Plants
- DOI
- 10.3390/rs17020285
- Published
- 01
- Container
- Remote Sensing
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.3390/rs17020285,
title = {Using Hyperspectral Imaging and Principal Component Analysis to Detect and Monitor Water Stress in Ornamental Plants},
author = {Van Patiluna and James Owen and Joe Mari Maja and Jyoti Neupane and Jan Behmann and David Bohnenkamp and Irene Borra-Serrano and José M. Peña and James Robbins and Ana de Castro},
year = {2025},
journal = {Remote Sensing},
doi = {10.3390/rs17020285},
url = {https://doi.org/10.3390/rs17020285}
}RIS
TY - JOUR TI - Using Hyperspectral Imaging and Principal Component Analysis to Detect and Monitor Water Stress in Ornamental Plants AU - Van Patiluna AU - James Owen AU - Joe Mari Maja AU - Jyoti Neupane AU - Jan Behmann AU - David Bohnenkamp AU - Irene Borra-Serrano AU - José M. Peña AU - James Robbins AU - Ana de Castro PY - 2025 JO - Remote Sensing DO - 10.3390/rs17020285 UR - https://doi.org/10.3390/rs17020285 ER -
APA
Patiluna, V., Owen, J., Maja, J. M., Neupane, J., Behmann, J., Bohnenkamp, D., Borra-Serrano, I., Peña, J. M., Robbins, J., & Castro, A. D. (2025). Using Hyperspectral Imaging and Principal Component Analysis to Detect and Monitor Water Stress in Ornamental Plants. Remote Sensing. https://doi.org/10.3390/rs17020285
Source records
- doaj · retrieved 2026-09-25T12:47:18.985Z