Hyperspectral Estimation of Chlorophyll Content in Apple Tree Leaf Based on Feature Band Selection and the CatBoost Model

Zhang, Yu; Chang, Qingrui; Chen, Yi; Liu, Yanfu; Jiang, Danyao; Zhang, Zijuan

doi:10.3390/agronomy13082075

Cited by 15 publications

(2 citation statements)

References 75 publications

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“…Therefore, to further explore more superior model parameter combinations, we input the SIs, SIs + R λ , and SIs + R λ + PPs into the multivariate linear model and the LCC-ML model, respectively. Research has shown that using SIs obtained from multiple spectral transformation methods rather than a single spectrum led to the better inversion of physiological and biochemical parameters [69]. Therefore, we utilized the SIs obtained from all spectral transformations, after feature variable selection by the GA, as model-independent variables.…”

Section: Estimating Chlorophyll Using Sis R λ and Ppsmentioning

confidence: 99%

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

Guo,

Jiang,

Miao

et al. 2024

Remote Sensing

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Accurately measuring leaf chlorophyll content (LCC) is crucial for monitoring maize growth. This study aims to rapidly and non-destructively estimate the maize LCC during four critical growth stages and investigate the ability of phenological parameters (PPs) to estimate the LCC. First, four spectra were obtained by spectral denoising followed by spectral transformation. Next, sensitive bands (Rλ), spectral indices (SIs), and PPs were extracted from all four spectra at each growth stage. Then, univariate models were constructed to determine their potential for independent LCC estimation. The multivariate regression models for the LCC (LCC-MR) were built based on SIs, SIs + Rλ, and SIs + Rλ + PPs after feature variable selection. The results indicate that our machine-learning-based LCC-MR models demonstrated high overall accuracy. Notably, 83.33% and 58.33% of these models showed improved accuracy when the Rλ and PPs were successively introduced to the SIs. Additionally, the model accuracies of the milk-ripe and tasseling stages outperformed those of the flare–opening and jointing stages under identical conditions. The optimal model was created using XGBoost, incorporating the SI, Rλ, and PP variables at the R3 stage. These findings will provide guidance and support for maize growth monitoring and management.

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Section: Estimating Chlorophyll Using Sis R λ and Ppsmentioning

confidence: 99%

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

Guo,

Jiang,

Miao

et al. 2024

Remote Sensing

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“…For instance, Yang et al [20] processed hyperspectral images using first-order derivatives, employed principal component analysis and the Successive Projections Algorithm (SPA) for dimensionality reduction, and constructed chlorophyll estimation models based on four types of regression models. Zhang et al [21] used feature selection methods like Competitive Adaptive Reweighted Sampling (CARS) to predict from second-order derivative-transformed hyperspectral images and constructed a CatBoostbased model for estimating chlorophyll content in apple trees to monitor their growth, with R 2 , RMSE, and RPD values of 0.923, 2.472, and 3.64, respectively. Chen et al [22] developed a model combining the Genetic Algorithm (GA) with the Partial Least Squares (PLS) method to select characteristic bands in rapeseed, resulting in an RPD increase of 3.42 compared to the original full-spectrum model, effectively reducing the number of model variables and enhancing model accuracy.…”

Section: Introductionmentioning

confidence: 99%

Inversion of Glycyrrhiza Chlorophyll Content Based on Hyperspectral Imagery

Xu,

Dai,

Zhang

et al. 2024

Agronomy

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Glycyrrhiza is an important medicinal crop that has been extensively utilized in the food and medical sectors, yet studies on hyperspectral remote sensing monitoring of glycyrrhiza are currently scarce. This study analyzes glycyrrhiza hyperspectral images, extracts characteristic bands and vegetation indices, and constructs inversion models using different input features. The study obtained ground and unmanned aerial vehicle (UAV) hyperspectral images and chlorophyll content (called Soil and Plant Analyzer Development (SPAD) values) from sampling sites at three growth stages of glycyrrhiza (regreening, flowering, and maturity). Hyperspectral data were smoothed using the Savitzky–Golay filter, and the feature vegetation index was selected using the Pearson Correlation Coefficient (PCC) and Recursive Feature Elimination (RFE). Feature extraction was performed using Competitive Adaptive Reweighted Sampling (CARS), Genetic Algorithm (GA), and Successive Projections Algorithm (SPA). The SPAD values were then inverted using Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), and the results were analyzed visually. The results indicate that in the ground glycyrrhiza inversion model, the GA-XGBoost model combination performed best during the regreening period, with R2, RMSE, and MAE values of 0.95, 0.967, and 0.825, respectively, showing improved model accuracy compared to full-spectrum methods. In the UAV glycyrrhiza inversion model, the CARS-PLSR combination algorithm yielded the best results during the maturity stage, with R2, RMSE, and MAE values of 0.83, 1.279, and 1.215, respectively. This study proposes a method combining feature selection techniques and machine learning algorithms that can provide a reference for rapid, nondestructive inversion of glycyrrhiza SPAD at different growth stages using hyperspectral sensors. This is significant for monitoring the growth of glycyrrhiza, managing fertilization, and advancing precision agriculture.

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Quantitative study on impact damage of honey peaches based on reflection, absorbance, and Kubelka‐Munk spectrum combined with color characteristics

Li,

Zou,

et al. 2024

Journal of Chemometrics

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Impact damage is one of the key factors affecting the quality of honey peaches. Quantitative study of impact damage is of great significance for the sorting of postharvest quality of honey peaches. In order to realize the quantitative prediction of impact damage of honey peaches, the impact damage of honey peaches was quantitatively studied based on the fusion of color characteristics with reflection spectra (R), absorbance spectra (A), and Kubelka‐Munk spectra (K‐M). The mechanical parameters of honey peaches during collision were obtained using a single pendulum collision device. Reflectance spectra and color characteristics of damaged honey peaches were obtained by a hyperspectral imaging system. The R spectra were converted into A and K‐M spectra, and the partial least squares regression (PLSR) model was built based on the three spectra and the three spectra combined with color characteristics for quantitative prediction of mechanical parameters. The results show that the prediction performance of the PLSR model is improved by combining color features with spectral information. In order to eliminate the redundant information in the spectral data, the competitive adaptive reweighted sampling (CARS) algorithm was used to select the characteristic wavelengths of the three spectra, and the selected characteristic wavelengths were fused with the color features to establish the PLSR model. The results show that the PLSR model built by the characteristic wavelengths of the A spectrum combined with the color features has the best prediction performance for the mechanical parameters. The RP value for maximum force is 0.862, and the RP value for damage depth is 0.894. The results of this study not only provide the theoretical support for the quality sorting, packaging, and transportation of honey peaches but also provide the reference for the biomechanical properties of various agricultural products.

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Hyperspectral Estimation of Chlorophyll Content in Apple Tree Leaf Based on Feature Band Selection and the CatBoost Model

Cited by 15 publications

References 75 publications

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

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

Inversion of Glycyrrhiza Chlorophyll Content Based on Hyperspectral Imagery

Quantitative study on impact damage of honey peaches based on reflection, absorbance, and Kubelka‐Munk spectrum combined with color characteristics

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