Narmilan, A.; Gonzalez, F.; Salgadoe, A.S.A.; Kumarasiri, U.W.L.M.; Weerasinghe, H.A.S.; Kulasekara, B.R.
Predicting Canopy Chlorophyll Content in Sugarcane Crops Using Machine Learning Algorithms and Spectral Vegetation Indices Derived from UAV Multispectral Imagery. Remote Sens. 2022, 14, 1140.
https://doi.org/10.3390/rs14051140
AMA Style
Narmilan A, Gonzalez F, Salgadoe ASA, Kumarasiri UWLM, Weerasinghe HAS, Kulasekara BR.
Predicting Canopy Chlorophyll Content in Sugarcane Crops Using Machine Learning Algorithms and Spectral Vegetation Indices Derived from UAV Multispectral Imagery. Remote Sensing. 2022; 14(5):1140.
https://doi.org/10.3390/rs14051140
Chicago/Turabian Style
Narmilan, Amarasingam, Felipe Gonzalez, Arachchige Surantha Ashan Salgadoe, Unupen Widanelage Lahiru Madhushanka Kumarasiri, Hettiarachchige Asiri Sampageeth Weerasinghe, and Buddhika Rasanjana Kulasekara.
2022. "Predicting Canopy Chlorophyll Content in Sugarcane Crops Using Machine Learning Algorithms and Spectral Vegetation Indices Derived from UAV Multispectral Imagery" Remote Sensing 14, no. 5: 1140.
https://doi.org/10.3390/rs14051140
APA Style
Narmilan, A., Gonzalez, F., Salgadoe, A. S. A., Kumarasiri, U. W. L. M., Weerasinghe, H. A. S., & Kulasekara, B. R.
(2022). Predicting Canopy Chlorophyll Content in Sugarcane Crops Using Machine Learning Algorithms and Spectral Vegetation Indices Derived from UAV Multispectral Imagery. Remote Sensing, 14(5), 1140.
https://doi.org/10.3390/rs14051140
