Estimation of the Relative Chlorophyll Content of Carya illinoensis Leaves Using Fractional Order Derivative of Leaf and Canopy Scale Hyperspectral Data

Journal of soil science and plant nutrition, Journal Year: 2024, Volume and Issue: 24(1), P. 1407 - 1423

Published: Feb. 12, 2024

Abstract Chlorophyll is a crucial physiological and biochemical indicator that impacts plant photosynthesis, accumulation of photosynthetic products, final yield. The measurement analysis chlorophyll content in plants can provide valuable insights into their nutritional status overall health. non-destructive efficient estimation relevant indicators using hyperspectral technology reliable method for collecting data on nutrient levels health during growth development. Fifty-three Carya illinoensis Jiande Changlin series known exceptional qualities significant economic benefits were used as the research object leaf canopy data. Firstly, fractional order derivative (FOD) was spectral preprocessing. Secondly, response relationship between spectrum relative (soil analyzer development, SPAD) explored by combining single-band two-band index (normalized difference index, NDSI). correlation coefficient Pearson to estimate linear variables. Finally, feature variables SPAD analyzed calculated. Top 10 absolute values coefficients screened out modeling eXtreme gradient boosting (XGBoost) machine learning algorithm construct optimal model leaves. Results showed after FOD pretreatment substantially improved, compared with raw spectrum. combined NDSI more effective than single band improving characteristics target components, which increased 0.166 0.338, respectively. could accurately 0.5th-order transformation (NDSI) model. R 2 P 0.788, RMSEP 0.842 prediction set. On one hand, this study confirms feasibility rapid leaves technology. other indices significantly improve variables, enrich processing methods, propose novel approach detection level

Language: Английский

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Can SPAD Values and CIE L*a*b* Scales Predict Chlorophyll and Carotenoid Concentrations in Leaves and Diagnose the Growth Potential of Trees? An Empirical Study of Four Tree Species

Horticulturae, Journal Year: 2024, Volume and Issue: 10(6), P. 548 - 548

Published: May 24, 2024

Photosynthetic pigments are fundamental for plant photosynthesis and play an important role in growth. Currently, the frequently used method measuring photosynthetic is spectrophotometry. Additionally, SPAD-502 chlorophyll meter, with its advantages of easy operation non-destructive testing, has been widely applied land agriculture. However, application prospects test results horticultural plants have not yet proven. This study examines reliability SPAD values predicting concentrations. Using fresh senescent leaves from four common plants, we measured values, pigment concentrations, leaf color parameters. A generalized linear mixed model demonstrated that a reliable indicator interspecific variations exist. Based on predictive power chlorophyll, first propose Enrichment Index (CEI) Normal Chlorophyll Concentration Threshold (NCCT). The CEI can be to compare among different species, NCCT value serve as more accurate assessing growth potential old trees. due limited sample size, further research larger samples needed refine diagnosis enhance management ornamental cultivation.

Language: Английский

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Citrus Huanglongbing Detection: A Hyperspectral Data-Driven Model Integrating Feature Band Selection with Machine Learning Algorithms

Crop Protection, Journal Year: 2024, Volume and Issue: 188, P. 107008 - 107008

Published: Oct. 31, 2024

Language: Английский

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In situ flexible wearable tomato growth sensor: monitoring of leaf physiological characteristics

Frontiers in Plant Science, Journal Year: 2025, Volume and Issue: 16

Published: March 21, 2025

In situ real-time monitoring of physiological information during crop growth (such as leaf chlorophyll values and water content) is crucial for enhancing agricultural production efficiency management practices. traditional monitoring, commonly used measurement methods, such chemical analysis determining drying methods measuring content, are all non- in techniques. These not only risk damaging the plants but may also impact plant health. Furthermore, complex setup spectrometers complicates data collection process, which limits their practical application monitoring. Therefore, there an urgent need to develop a novel, user friendly, plant-safe technology improve efficiency. To this end, study proposes novel wearable flexible sensor designed content. This lightweight, portable, allows placement, enabling continuous by conforming surfaces. Its spectral response covers multiple bands from near ultraviolet infrared, it equipped with active light source ranging infrared enable efficient measurements under various environmental conditions. addition, securely attached underside using magnetic suction method, ensuring long-term stable thus continuously collecting important throughout cycle. Analysis sensor-collected reveals that chlorophyll, Gaussian process regression shows best prediction performance multi-spectral scattering correction, R c 2 0.8261 RMSEc 1.7444 on training set; test set Rp² 0.7155 RMSE p 2.0374. Meanwhile, across preprocessing scenarios, gradient boosting can effectively predict it, yielding Rc² 0.9401 0.0028 0.6667 0.0067.

Language: Английский

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Analysis of the Effects of Different Spectral Transformation Methods on the Estimation of Chlorophyll Content of Reclaimed Vegetation in Rare Earth Mining Areas

Forests, Journal Year: 2024, Volume and Issue: 16(1), P. 26 - 26

Published: Dec. 26, 2024

Ion adsorption rare earths are an important strategic resource, but their leach mining causes post-mining wastelands and tailings to suffer from soil sanding, acidification, heavy metal contamination. This makes natural vegetation recovery difficult, relying mainly on artificial reclamation; however, the reclaimed grows poorly due environmental stress. Hyperspectral remote sensing technology, with its high efficiency, non-destructive nature, wide-range monitoring capability, can accurately estimate physiological parameters of vegetation. provides support for regulation in areas. In this study, three typical types Lingbei Rare Earth Mining Area, Dingnan County, Ganzhou City, were analyzed. data corresponding chlorophyll content collected compare spectral differences between normal The processed using mathematical transformation, fractional order differentiation, discrete wavelet transform, continuous transform. Sensitive bands extracted, multispectral transformed feature integrated. Linear machine learning regression models used content. effects different processing methods estimation then results showed that had higher reflectance than vegetation, red valley shifting towards long-wave direction a steeper edge slope. Different transformation impact accuracy estimation. Using appropriate improve accuracy. Fusing multi-spectral features achieve relatively good results. Among models, random forest model best performance estimating study scientific basis rapid accurate growth earth areas, supporting management decision-making contributing ecological restoration.

Language: Английский

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Mapping of the Spatio-Spectral Dynamics of Mangrove Chlorophyll Concentrations via Sentinel-2 Satellite Imagery

Forum Geografi, Journal Year: 2024, Volume and Issue: 38(2), P. 244 - 256

Published: Aug. 29, 2024

Mangrove ecosystems play a critical role in maintaining coastal health; however, they are increasingly threatened by anthropogenic activities and climate change. Health assessment is essential for effective conservation efforts. However, traditional remote sensing techniques such as the normalised difference vegetation index (NDVI) may not fully capture complex physiological processes influencing health. Therefore, this study investigated chlorophyll (Chl) dynamics mangroves using techniques, including NDVI novel method, area over reflectance curve (NAOC), via Sentinel-2 satellite imagery during October 2023, analysed spatial variations Chl content (CC) Google Earth Engine API. NAOC-Chl were weakly correlated (0.47), highlighting their complementary roles. The average NOAC-Chl values different species analysed, Rhizophora mucronata presented highest value (NDVI: 0.86 ± 0.08, NOAC: 20.48 4.49.

Language: Английский

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Spectral Variations of Reclamation Vegetation in Rare Earth Mining Areas Using Continuous–Discrete Wavelets and Their Impact on Chlorophyll Estimation

Forests, Journal Year: 2024, Volume and Issue: 15(11), P. 1885 - 1885

Published: Oct. 26, 2024

Ion-adsorption rare earth mining areas are primarily situated in the hilly regions of southern China. However, activities have led to extensive deforestation original vegetation. The reclamation vegetation planted for ecological restoration faces significant challenges surviving under environmental stresses, including heavy metal pollution, ammonia nitrogen contamination, and soil drought. To rapidly accurately monitor growth vegetation, this study investigates spectral variations their impact on accuracy chlorophyll estimation, utilizing hyperspectral data relative content (SPAD). Specifically, continuous–discrete wavelet transforms were applied, along with spectra first derivative spectra, enhance anomalies identify chlorophyll-sensitive features. Additionally, multiple linear stepwise regression backpropagation neural network models employed estimate content. results revealed following: (1) d5 d6 scales discrete effectively highlighted vegetation; (2) Salix japonica (Salix fragilis L.), among typical species, exhibited poor adaptability conditions area; (3) model demonstrated superior performance features Fir, Fir_d4, Fir_d5, Fir_d6 significantly enhancing model, achieving an R2 0.93 Photinia glabra (Photinia (Thunb.) Maxim.). application improves precision underscoring potential method rapid monitoring growth.

Language: Английский

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Estimating Aboveground Biomass of Wetland Plant Communities from Hyperspectral Data Based on Fractional-Order Derivatives and Machine Learning

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(16), P. 3011 - 3011

Published: Aug. 16, 2024

Wetlands, as a crucial component of terrestrial ecosystems, play significant role in global ecological services. Aboveground biomass (AGB) is key indicator the productivity and carbon sequestration potential wetland ecosystems. The current research methods for remote-sensing estimation either rely on traditional vegetation indices or merely perform integer-order differential transformations spectra, failing to fully leverage information complexity hyperspectral data. To identify an effective method estimating AGB mixed-wetland-plant communities, we conducted field surveys from three typical wetlands within Crested Ibis National Nature Reserve Hanzhong, Shaanxi, concurrently acquired canopy data with portable spectrometer. spectral features were transformed by applying fractional-order differentiation (0.0 2.0) extract optimal feature combinations. prediction models built using machine learning models, XGBoost, Random Forest (RF), CatBoost, accuracy each model was evaluated. combination differentiation, indices, importance effectively yielded combinations, integrating bands enhanced predictive models. Among machine-learning RF achieved superior 0.8-order transformation (R2 = 0.673, RMSE 23.196, RPD 1.736). visually interpreted Shapley Additive Explanations, which revealed that contribution varied across individual sample predictions. Our study provides methodological technical support monitoring AGB.

Language: Английский

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