Nutrient distribution in various tissues of Licorice (Glycyrrhiza glabra L.) and the influence of soil fertility on the levels of its bioactive compounds

Goudarzi, Tahereh; Tabrizi, Leila; Nazeri, Vahideh; Etemadi, Mohammad

doi:10.1016/j.indcrop.2024.118073

Cited by 4 publications

(1 citation statement)

References 76 publications

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“…Due to significant developmental differences among glycyrrhiza varieties, and variations in growth characteristics and environmental influences, the distribution of SPAD values varies across different growth stages. Nitrogen levels and soil moisture have a substantial impact on the growth of glycyrrhiza [52,53]. This study maintains consistent nitrogen application and irrigation levels across the experimental field, disregarding declines in chlorophyll content due to drought stress or insufficient nutrient levels.…”

Section: Chlorophyll Distribution During Different Growth Stages Of G...supporting

confidence: 56%

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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Section: Chlorophyll Distribution During Different Growth Stages Of G...supporting

confidence: 56%

Inversion of Glycyrrhiza Chlorophyll Content Based on Hyperspectral Imagery

Xu,

Dai,

Zhang

et al. 2024

Agronomy

View full text Add to dashboard Cite

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Growth, yield, and biochemical traits of tomato plants under mycorrhizal inoculation and licorice root extract applications

Halshoy

2024

Soil Science and Plant Nutrition

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Sunflower Origin Identification Based on Multi-Source Information Fusion Technique of Kernel Extreme Learning Machine

Suo,

Liu,

et al. 2024

Agronomy

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This study constructs a model for the rapid identification of the origins of edible sunflower (Helianthus) using Kernel Extreme Learning Machine (KELM) with multi-source information fusion technology. Near-infrared spectroscopy (NIRS) and nuclear magnetic resonance spectroscopy (NMRS) were utilized to analyze 180 sunflower samples from the Xinjiang, Heilongjiang, and Inner Mongolia regions. Initially, the identification models for the origin of sunflowers using NIR and NMR data were compared between two algorithms: the Extreme Learning Machine (ELM) and KELM, combined with various spectral preprocessing methods. The experiment found that the NIR spectral model preprocessed with standard normal variate (SNV) using the KELM algorithm was the most accurate, achieving accuracies of 98.7% in the training set and 97.2% in the test set. The spin-echo NMR spectral model preprocessed with non-local means (NLMs) using the KELM algorithm was the second best, with accuracies of 98.4% in the training set and 96.4% in the test set. To further improve the accuracy of the identification models, innovative sunflower origin identification models were developed based on data layer fusion and feature layer fusion using NIRS and NMRS. In the data layer fusion model, the KELM algorithm model was optimal, achieving a test set accuracy and F1 score of 98.2% and 98.18%, respectively, an improvement of 1.0% over the best single data source model. In the feature layer fusion model, four types of feature-layer information-fusion identification models were established using two feature extraction algorithms, Competitive Adaptive Reweighted Sampling (CARS) and Variable Importance Projection (VIP), combined with joint feature and simple merging feature strategies. The CARS-KELM algorithm combined with the joint feature method was found to be the best, achieving 100% accuracy in both the training and test sets, an improvement of 2.8% over the best single data source model. Identifying the origin of edible sunflower using NIRS and NMRS is demonstrated as feasible by the results. The best single-spectrum sunflower origin identification model was achieved using the KELM algorithm with SNV preprocessing. The feature layer fusion method combining NIRS and NMRS data is suitable for handling the task of sunflower origin identification. This method significantly improves the recognition accuracy of the model compared to a single model, achieving fast and accurate origin identification of edible sunflowers. The research results provide a new method for rapid identification of sunflower origin.

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Nutrient distribution in various tissues of Licorice (Glycyrrhiza glabra L.) and the influence of soil fertility on the levels of its bioactive compounds

Cited by 4 publications

References 76 publications

Inversion of Glycyrrhiza Chlorophyll Content Based on Hyperspectral Imagery

Inversion of Glycyrrhiza Chlorophyll Content Based on Hyperspectral Imagery

Growth, yield, and biochemical traits of tomato plants under mycorrhizal inoculation and licorice root extract applications

Sunflower Origin Identification Based on Multi-Source Information Fusion Technique of Kernel Extreme Learning Machine

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