Combining Different Transformations of Ground Hyperspectral Data with Unmanned Aerial Vehicle (UAV) Images for Anthocyanin Estimation in Tree Peony Leaves

Abstract

To explore rapid anthocyanin (Anth) detection technology based on remote sensing (RS) in tree peony leaves, we considered 30 species of tree peonies located in Shaanxi Province, China. We used an SVC HR~1024i portable ground object spectrometer and mini-unmanned aerial vehicle (UAV)-borne RS systems to obtain hyperspectral (HS) reflectance and images of canopy leaves. First, we performed principal component analysis (PCA), first-order differential (FD), and continuum removal (CR) transformations on the original ground-based spectra; commonly used spectral parameters were implemented to estimate Anth content using multiple stepwise regression (MSR), partial least squares (PLS), back-propagation neural network (BPNN), and random forest (RF) models. The spectral transformation highlighted the characteristics of spectral curves and improved the relationship between spectral reflectance and Anth, and the RF model based on the FD spectrum portrayed the best estimation accuracy (R2c = 0.91; R2v = 0.51). Then, the RGB (red-green-blue) gray vegetation index (VI) and the texture parameters were constructed using UAV images, and an Anth estimation model was constructed using UAV parameters. Finally, the UAV image was fused with the ground spectral data, and a multisource RS model of Anth estimation was constructed, based on PCA + UAV, FD + UAV, and CR + UAV, using MSR, PLS, BPNN, and RF methods. The RF model based on FD+UAV portrayed the best modeling and verification effect (R2c = 0.93; R2v = 0.76); compared with the FD-RF model, R2c increased only slightly, but R2v increased greatly from 0.51 to 0.76, indicating improved modeling and testing accuracy. The optimal spectral transformation for the Anth estimation of tree peony leaves was obtained, and a high-precision Anth multisource RS model was constructed. Our results can be used for the selection of ground-based HS transformation in future plant Anth estimation, and as a theoretical basis for plant growth monitoring based on ground and UAV multisource RS.

Ground-Based Hyperspectral Estimation of Maize Leaf Chlorophyll Content Considering Phenological Characteristics

“…Finally, all spectral curves were resampled to 1 nm in the SVC HR1024i software. Based on previous studies [14,18,35], the spectral range of this study was determined to be 360-1000 nm. Figure 2a shows the SVC HR-1024i device.…”

“…Since the most practical combination of spectral parameters for estimating the maize LCC was unknown, and considering the superiority of machine learning regression (MLR) in estimating crop physiological and biochemical parameters [8,35,52], we developed the LCC-ML model utilizing the RF, SVM, and XGBoost algorithms. We combined ten selected SIs determined by the GA with the R λ and PPs as independent variables.…”

A Review of Hybrid Approaches for Quantitative Assessment of Crop Traits Using Optical Remote Sensing: Research Trends and Future Directions

“…For instance, in the study of [99], the Chlorophyll Absorption Continuum Index (CACI) was developed and calculated, based on computing the area under the spectral curve between 550 and 730 nm. Other studies also used this technique for enhancing the accuracy of crop traits (LAI, nitrogen, and chlorophyll) [97,100,101]. The wavelet transform (WT) method is a viable method for analyzing the spectrum that converts the original reflectance spectrum into coefficients resolving at high scales (e.g., small narrow bandwidth absorption features) and low scales (e.g., broad absorption features).…”

Iron-copper interaction in soil spectra and its effect on the estimation of copper content

“…), also influenced by the difference in response of the spectrometer itself to different wavelengths, these make the correlation between the original spectral reflectance and the measured object is low and does not meet the demand of spectral prediction. Therefore, the first order differential (FDR) pre-processing transform is performed on the soil spectral data (Luo, et al, 2022) Eq. 1.…”