Retrieval of Leaf Chlorophyll Contents (LCCs) in Litchi Based on Fractional Order Derivatives and VCPA-GA-ML Algorithms

Abstract

The accurate estimation of leaf chlorophyll content (LCC) is a significant foundation in assessing litchi photosynthetic activity and possible nutrient status. Hyperspectral remote sensing data have been widely used in agricultural quantitative monitoring research for the non-destructive assessment of LCC. Variable selection approaches are crucial for analyzing high-dimensional datasets due to the high danger of overfitting, time-intensiveness, or substantial computational requirements. In this study, the performance of five machine learning regression algorithms (MLRAs) was investigated based on the hyperspectral fractional order derivative (FOD) reflection of 298 leaves together with the variable combination population analysis (VCPA)-genetic algorithm (GA) hybrid strategy in estimating the LCC of Litchi. The results showed that the correlation coefficient (r) between the 0.8-order derivative spectrum and LCC had the highest correlation coefficients (r = 0.9179, p < 0.01). The VCPA-GA hybrid strategy fully utilizes VCPA and GA while compensating for their limitations based on a large number of variables. Moreover, the model was developed using the selected 14 sensitive bands from 0.8-order hyperspectral reflectance data with the lowest root mean square error in prediction (RMSEP = 5.04 μg∙cm−2). Compared with the five MLRAs, validation results confirmed that the ridge regression (RR) algorithm derived from the 0.2 order was the most effective for estimating the LCC with the coefficient of determination (R2 = 0.88), mean absolute error (MAE = 3.40 μg∙cm−2), root mean square error (RMSE = 4.23 μg∙cm−2), and ratio of performance to inter-quartile distance (RPIQ = 3.59). This study indicates that a hybrid variable selection strategy (VCPA-GA) and MLRAs are very effective in retrieving the LCC through hyperspectral reflectance at the leaf scale. The proposed methods could further provide some scientific basis for the hyperspectral remote sensing band setting of different platforms, such as an unmanned aerial vehicle (UAV) and satellite.

Estimation and Mapping of Soil Organic Matter Content Using a Stacking Ensemble Learning Model Based on Hyperspectral Images

“…Mathematical transformation methods, such as spatial and frequency domains for image denoising and spectral pre-processing effects, are regarded as an effective approach to eliminate pixel spectral noise, amplify the peak and valley variations in the spectral curves, and enhance the spectral characteristics of the soil [37][38][39]. The first-order differential (FD-R) transformation can improve the signal-to-noise ratio of the response spectral bands by eliminating baseline drift and improving spectral resolution [40]; the fractionalorder differential (FOD) transformation is considered an efficient method for the peak and valley amplification of the spectra by capturing increasing subtle spectral information [41]; the Savitzky-Golay (SG) filter can remove spectral noise of hyperspectral remote sensing images in the spectral domain by smoothing spectral curves and dividing the original reflectance spectra and the envelope [42]; the continuum removal (CR) method is characterized by the highlights of the absorption and reflection features of the spectral curves, significantly diminishing the influence of terrain and illumination conditions on the spectral intensity and the absorption depth [43]. The spectral reflectance curves of hyperspectral images after pre-processing are shown in Figure 4.…”

Indirect Estimation of Heavy Metal Contamination in Rice Soil Using Spectral Techniques

“…The reason for this approach is that spectral data have properties of redundancy and collinearity, and direct modelling with PLSR is susceptible to being disturbed by significant amounts of redundancy information [ 36 ]. GA improves model quality and stability by successfully filtering feature bands from the full spectrum [ 37 ]. The results of Sun and Zhang [ 38 ], Sun et al [ 39 ], and Zhong et al [ 17 ], who also showed that GA-PLSR outperforms PLSR in estimating heavy metal concentration using soil spectral data, are consistent with this methodology.…”

Comparison of feature selection and data fusion of Fourier transform infrared and Raman spectroscopy for identifying watercolor ink

“…The variable combination cluster analysis (VCPA) algorithm optimizes the variable space by applying an exponential reduction function (EDF). Based on the model population analysis strategy, the combination of evaluation variables according to RMSECV [26]. The genetic algorithm (GA) and iterations retain information variables (IRIV) algorithm can effectively eliminate uninformative variables and optimize them globally.…”

“…However, their time complexity is very high when faced with high‐dimensional spectral data. This experiment used the VCPA‐GA coupling algorithm and VCPA‐IRIV coupling algorithm to select the characteristic wavelengths based on the advantages of VCPA, GA, and IRIV [26, 27]. The mean number of each BMS sampling is 20 in the VCPA step.…”