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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.

• FOD indices are more robust for estimating leaf photosynthetic parameters. • 1.50- and 1.60-order D-type indices performed best for Vcmax and Jmax estimations. • High order-derived indices are more sensitive to random noise. The maximum carboxylation rate (Vcmax) and the maximum electron transport rate (Jmax) are representative parameters describing the gas exchange properties of leaves. Non-destructive measurements of these parameters are valuable for tracing the physiological status and productivity of a plant. The spectral vegetation index, as a simple and efficient approach, has been applied to estimate these two parameters. However, most indices used are derived from original or integer-order rather than fractional-order derivatives (FOD), despite increasing evidence of their advantages in terms of tracing diverse plant properties. In this study, we attempt to derive photosynthetic capacity parameters from FOD indices based on a composite hyperspectral spectroscopy dataset covering various conditions. Our results show that the FOD can be utilized to develop more robust indices for obtaining photosynthetic capacity parameters. In particular, the D-type indices calculated from the 1.50-order and 1.60-order derivatives produced the highest accuracies for the estimation of Vcmax and Jmax. The performance of these proposed indices, however, differed for different plant functional types and had a high sensitivity to random noise. The results of the present research show that FOD indices have the potential to estimate leaf photosynthetic parameters, and are therefore to support hyperspectral information on estimating plant properties. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]