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Open AccessFeature PaperArticle

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

1
Geospatial Institute, Saint Louis University, Saint Louis, MO 63108, USA
2
Department of Earth and Atmospheric Sciences, Saint Louis University, Saint Louis, MO 63108, USA
3
The School of Plant Sciences, University of Arizona, Tucson, AZ 85721, USA
4
Donald Danforth Plant Science Center, Saint Louis, MO 63132, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(13), 2082; https://doi.org/10.3390/rs12132082
Received: 24 May 2020 / Revised: 21 June 2020 / Accepted: 24 June 2020 / Published: 29 June 2020
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 hyperspectral data with derivative calculus and machine learning to estimate LCC of sorghum. We calculated fractional derivative (FD) orders starting from 0.2 to 2.0 with 0.2 order increments. Additionally, 43 common vegetation indices (VIs) were calculated from leaf spectral reflectance factor to make comparisons with reflectance-based data. Within the modeling pipeline, three feature selection methods were assessed: Pearson’s correlation coefficient (PCC), partial least squares based variable importance in the projection (VIP), and random forest-based mean decrease impurity (MDI). Finally, we used partial least squares regression (PLSR), random forest regression (RFR), support vector regression (SVR), and extreme learning regression (ELR) to estimate the LCC of sorghum. Results showed that: (1) increasing derivative order can show improved model performance until certain order for reflectance-based analysis; however, it is inconclusive to state that a particular order is optimal for estimating LCC of sorghum; (2) VI-based modeling outperformed derivative augmented reflectance factor-based modeling; (3) mean decrease impurity was found effective in selecting sensitive features from large feature space (reflectance-based analysis), whereas simple Pearson’s correlation coefficient worked better with smaller feature space (VI-based analysis); and (4) SVR outperformed all other models within reflectance-based analysis; alternatively, ELR with VIs from original reflectance yielded slightly better results compared to all other models. View Full-Text
Keywords: chlorophyll concentration; fractional derivatives; hyperspectral spectroscopy; machine learning; extreme learning regression chlorophyll concentration; fractional derivatives; hyperspectral spectroscopy; machine learning; extreme learning regression
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MDPI and ACS Style

Bhadra, S.; Sagan, V.; Maimaitijiang, M.; Maimaitiyiming, M.; Newcomb, M.; Shakoor, N.; Mockler, T.C. Quantifying Leaf Chlorophyll Concentration of Sorghum from Hyperspectral Data Using Derivative Calculus and Machine Learning. Remote Sens. 2020, 12, 2082.

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