ArticlePlant methods2024
Combining the fractional order derivative and machine learning for leaf water content estimation of spring wheat using hyper-spectral indices.
Article in Plant methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- Near-Infrared Spectroscopy Non-Destructive Detection Modeling for Starch Content in Kernels of 58 Rainfed Corn Varieties.Foods (Basel, Switzerland) · 2026Article
- PlantSpecLab: A comprehensive open-source platform for high-throughput plant spectral data processing and phenotypic modeling.Plant phenomics (Washington, D.C.) · 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
- Interaction of Soil Texture and Irrigation Level Improves Mesophyll Conductance Estimation.Plants (Basel, Switzerland) · 2025Article
- VNIR Hyperspectral Signatures for Early Detection and Machine-Learning Classification of Wheat Diseases.Plants (Basel, Switzerland) · 2025Article
- Hyperspectral Imaging-Based Deep Learning Method for Detecting Quarantine Diseases in Apples.Foods (Basel, Switzerland) · 2025Article
- The PLSR-ML fusion strategy for high-accuracy leaf potassium inversion in karst region of Southwest China.Frontiers in plant science · 2025Article
- Estimation of potato canopy leaf water content in various growth stages using UAV hyperspectral remote sensing and machine learning.Frontiers in plant science · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Leaf water content (LWC) is a vital indicator of crop growth and development. While visible and near-infrared (VIS-NIR) spectroscopy makes it possible to estimate crop leaf moisture, spectral preprocessing and multiband spectral indices have important significance in the quantitative analysis of LWC. In this work, the fractional order derivative (FOD) was used for leaf spectral processing, and multiband spectral indices were constructed based on the band-optimization algorithm. Eventually, an integrated index, namely, the multiband spectral index (MBSI) and moisture index (MI), is proposed to estimate the LWC in spring wheat around Fu-Kang City, Xinjiang, China. The MBSIs for LWC were calculated from two types of spectral data: raw reflectance (RR) and the spectrum based on FOD. The LWC was estimated by combining machine learning (K-nearest neighbor, KNN; support vector machine, SVM; and artificial neural network, ANN). The results showed that the fractional derivative pretreatment of spectral data enhances the implied information of the spectrum (the maximum correlation coefficient appeared using a 0.8-order differential) and increases the number of sensitive bands, especially in the near-infrared bands (700-1100 nm). The correlations between LWC and the two-band index (RVI
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