Predicting Canopy Chlorophyll Content in Sugarcane Crops Using Machine Learning Algorithms and Spectral Vegetation Indices Derived from UAV Multispectral Imagery

Amarasingam Narmilan, Felipe Gonzalez, Arachchige Surantha Ashan Salgadoe, Unupen Widanelage Lahiru Madhushanka Kumarasiri, Hettiarachchige Asiri Sampageeth Weerasinghe, Buddhika Rasanjana Kulasekara. 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, 2022. [doi]

@article{NarmilanGSKWK22,
  title = {Predicting Canopy Chlorophyll Content in Sugarcane Crops Using Machine Learning Algorithms and Spectral Vegetation Indices Derived from UAV Multispectral Imagery},
  author = {Amarasingam Narmilan and Felipe Gonzalez and Arachchige Surantha Ashan Salgadoe and Unupen Widanelage Lahiru Madhushanka Kumarasiri and Hettiarachchige Asiri Sampageeth Weerasinghe and Buddhika Rasanjana Kulasekara},
  year = {2022},
  doi = {10.3390/rs14051140},
  url = {https://doi.org/10.3390/rs14051140},
  researchr = {https://researchr.org/publication/NarmilanGSKWK22},
  cites = {0},
  citedby = {0},
  journal = {Remote Sensing},
  volume = {14},
  number = {5},
  pages = {1140},
}