Figure 1.
Positions of sampled leaf, stem, and root samples in one exemplary pepper plant.
Table 1.
Parameters of hyperspectral imaging device for acquisition of images.
Figure 2.
The flowchart of the Random frog algorithm from Li et al. [45].
Table 2.
Parameters for running random frog.
Table 3.
Statistic results of all samples’ TNCs-DC.
Table 4.
Summary of statistical analyses of TNCs-DC in calibration and prediction sets.
Figure 3.
Spectral curves of all pepper plant samples covering the range of 420–1,000 nm.
(a) mean spectral reflectance curves of all leaves and stems in upper, middle, and lower positions; (b) mean spectral reflectance and standard deviation (SD) of leaves, stems, and roots across all samples.
Figure 4.
Selection probability (SP) of each wavelength averaged over 50 runs of Random frog for TNCs-HSI prediction of (a) leaves, (b) stems, (c) roots, and (d) whole-plant samples (leaf-stem-root).
Table 5.
Important wavelengths for predicting TNCs in leaves, stems, roots, and whole-plant (leaf-stem-root) based on Random frog (RF).
Table 6.
Results of PLSR models for TNCs-HSI analysis based on full spectra (F-PLSR) and the selected important wavelengths (RF-PLSR).
Figure 5.
Spatial distribution maps of TNCs-HSI in samples of an exemplary tested pepper plant included six leaves, three stems, and one root, respectively.
TNCs-HSI of samples in hyperspectral images were computed based on linear function (2) and TNCs-HSI distribution was achieved in MATLAB software. The numbers accompanying each sample map denote the respective TNC-DC value.