Predicting Canopy Chlorophyll Content in Sugarcane Crops Using Machine Learning Algorithms and Spectral Vegetation Indices Derived from UAV Multispectral Imagery. | D2R Server publishing the DBLP Bibliography Database, hosted at L3S Research Center

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dc:creator	<https://dblp.l3s.de/d2r/resource/authors/Felipe_Gonzalez>
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dc:creator	<https://dblp.l3s.de/d2r/resource/authors/Unupen_Widanelage_Lahiru_Madhushanka_Kumarasiri>
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dcterms:issued	2022 (xsd:gYear)
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rdfs:label	Predicting Canopy Chlorophyll Content in Sugarcane Crops Using Machine Learning Algorithms and Spectral Vegetation Indices Derived from UAV Multispectral Imagery. (xsd:string)
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dc:title	Predicting Canopy Chlorophyll Content in Sugarcane Crops Using Machine Learning Algorithms and Spectral Vegetation Indices Derived from UAV Multispectral Imagery. (xsd:string)
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