ArticlePlant methods2021
Comparison of new hyperspectral index and machine learning models for prediction of winter wheat leaf water content.
Article in Plant methods, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed.
- Synchronized UAV multi-angle inversion of canopy structure parameters in wheat breeding materials.Plant phenomics (Washington, D.C.) · 2026Article
- Novel indices and multi-source data fusion for monitoring plant moisture stress in winter wheat fields.Scientific reports · 2026Article
- Non-destructive estimation of maize carotenoids using reflectance-based spectral indices.Frontiers in plant science · 2026Article
- Hyperspectral inversion of winter wheat nitrogen content based on feature optimization and stacking ensemble learning.Frontiers in plant science · 2026Article
- SENet-SVWF: a spectral vegetation indices and wavelet features fusion method using squeeze and excitation network for predicting the SPAD value of maize leaves.BMC plant biology · 2025Article
- Risk assessment of corn borer based on feature optimization and weighted spatial clustering: a case study in Shandong Province, China.Scientific reports · 2025Article
- Inversion and validation of soil water-holding capacity in a wild fruit forest, using hyperspectral technology combined with machine learning.Scientific reports · 2025Article
- Combining the fractional order derivative and machine learning for leaf water content estimation of spring wheat using hyper-spectral indices.Plant methods · 2024Article
- Estimation of potato canopy leaf water content in various growth stages using UAV hyperspectral remote sensing and machine learning.Frontiers in plant science · 2024Article
- Design and Experiment of a Portable Near-Infrared Spectroscopy Device for Convenient Prediction of Leaf Chlorophyll Content.Sensors (Basel, Switzerland) · 2023Article
- Plant optics: underlying mechanisms in remotely sensed signals for phenotyping applications.AoB PLANTS · 2023Review
- Based on machine learning algorithms for estimating leaf phosphorus concentration of rice using optimized spectral indices and continuous wavelet transform.Frontiers in plant science · 2023Article
- Hybrid retrieval of crop traits from multi-temporal PRISMA hyperspectral imagery.ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing (ISPRS) · 2022Article
- Hyperspectral Characteristics of an Individual Leaf of Wheat Grown under Nitrogen Gradient.Plants (Basel, Switzerland) · 2021Article
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7 authors.
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Abstract
backgroundThe leaf water content estimation model is established by hyperspectral technology, which is crucial and provides technical reference for precision irrigation.
methodsIn this study, two consecutive years of field experiments (different irrigation times and seven wheat varieties) in 2018-2020 were performed to obtain the canopy spectra reflectance and leaf water content (LWC) data. The characteristic bands related to LWC were extracted from correlation coefficient method (CA) and x-Loading weight method (x-Lw). Five modeling methods, spectral index and four other methods (Partial Least-Squares Regression (PLSR), Random Forest Regression (RFR), Extreme Random Trees (ERT), and K-Nearest Neighbor (KNN)) based characteristic bands, were employed to construct LWC estimation models.
resultsThe results showed that the canopy spectral reflectance increased with the increase of irrigation times, especially in the near-infrared band (750-1350 nm). The prediction accuracy of the newly developed differential spectral index DVI (R1185, R1307) was higher than that of the existing spectral index, with R
conclusionThe two models based on ERT-x-Lw and DVI (R1185, R1307) can effectively predict wheat leaf water content. The results provide a technical reference and a basis for crop water monitoring and diagnosis under similar production conditions.
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