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

The use of satellite-based Remote Sensing (RS) is a well-developed field of research. RS techniques have been successfully utilized to evaluate the chlorophyll content for the monitoring of sugarcane crops. This research provides a new framework for inferring the chlorophyll content in sugarcane cro...

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Hlavní autoři:	Amarasingam Narmilan, Felipe Gonzalez, Arachchige Surantha Ashan Salgadoe, Unupen Widanelage Lahiru Madhushanka Kumarasiri, Hettiarachchige Asiri Sampageeth Weerasinghe, Buddhika Rasanjana Kulasekara
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	MDPI AG 2022-02-01
Edice:	Remote Sensing
Témata:	chlorophyll machine learning multispectral imagery remote sensing sugarcane UAV
On-line přístup:	https://www.mdpi.com/2072-4292/14/5/1140
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