Quantifying Leaf Chlorophyll Concentration of Sorghum from Hyperspectral Data Using Derivative Calculus and Machine Learning

Quantifying Leaf Chlorophyll Concentration of Sorghum from Hyperspectral Data Using Derivative Calculus and Machine Learning

Leaf chlorophyll concentration (LCC) is an important indicator of plant health, vigor, physiological status, productivity, and nutrient deficiencies. Hyperspectral spectroscopy at leaf level has been widely used to estimate LCC accurately and non-destructively. This study utilized leaf-level hypersp...

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Bibliographic Details
Main Authors: Sourav Bhadra, Vasit Sagan, Maitiniyazi Maimaitijiang, Matthew Maimaitiyiming, Maria Newcomb, Nadia Shakoor, Todd C. Mockler
Format: Article
Language:English
Published: MDPI AG 2020-06-01
Series:Remote Sensing
Subjects:
chlorophyll concentration
fractional derivatives
hyperspectral spectroscopy
machine learning
extreme learning regression
Online Access:https://www.mdpi.com/2072-4292/12/13/2082

Internet

https://www.mdpi.com/2072-4292/12/13/2082

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