Benaragama, D.; Hussain, M.; Senetza, B.; Shirtliffe, S.; Willenborg, C.
UAV-Based Multispectral Phenotyping and Machine-Learning Modeling Reveals Early Canopy Traits as Strong Predictors of Yield and Weed Competitiveness in Oat (Avena sativa L.). Remote Sens. 2026, 18, 1211.
https://doi.org/10.3390/rs18081211
AMA Style
Benaragama D, Hussain M, Senetza B, Shirtliffe S, Willenborg C.
UAV-Based Multispectral Phenotyping and Machine-Learning Modeling Reveals Early Canopy Traits as Strong Predictors of Yield and Weed Competitiveness in Oat (Avena sativa L.). Remote Sensing. 2026; 18(8):1211.
https://doi.org/10.3390/rs18081211
Chicago/Turabian Style
Benaragama, Dilshan, Mujahid Hussain, Brianna Senetza, Steve Shirtliffe, and Chris Willenborg.
2026. "UAV-Based Multispectral Phenotyping and Machine-Learning Modeling Reveals Early Canopy Traits as Strong Predictors of Yield and Weed Competitiveness in Oat (Avena sativa L.)" Remote Sensing 18, no. 8: 1211.
https://doi.org/10.3390/rs18081211
APA Style
Benaragama, D., Hussain, M., Senetza, B., Shirtliffe, S., & Willenborg, C.
(2026). UAV-Based Multispectral Phenotyping and Machine-Learning Modeling Reveals Early Canopy Traits as Strong Predictors of Yield and Weed Competitiveness in Oat (Avena sativa L.). Remote Sensing, 18(8), 1211.
https://doi.org/10.3390/rs18081211
