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Related Experiment Video

Updated: Apr 28, 2026

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
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Cross-modal data integration and spectral optimization for enhanced individual apple tree canopy nitrogen

Bo Chen1,2, Ning Zhang1,2, Yanqing Li3

  • 1Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing, China.

Plant Phenomics (Washington, D.C.)
|April 27, 2026
PubMed
Summary

This study introduces a new method for precisely monitoring tree nitrogen levels in orchards using hyperspectral imaging. The framework improves canopy nitrogen concentration (CNC) estimation by accounting for canopy layers and pixel effects.

Keywords:
Canopy nitrogen distributionIndividual tree segmentationPixel clusteringUnmanned Aerial Vehicle (UAV)Wavelet enhancement

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Area of Science:

  • Agricultural remote sensing
  • Plant physiology
  • Geospatial analysis

Background:

  • Precision agriculture demands accurate, non-destructive monitoring of individual tree canopy nitrogen concentration (CNC) in high-density orchards.
  • Existing hyperspectral imaging methods face challenges due to unmodeled vertical CNC stratification and mixed-pixel effects at canopy boundaries.

Purpose of the Study:

  • To develop and validate a cross-modal framework integrating RGB-derived 3D point clouds and hyperspectral data for precise, individual-tree CNC estimation.
  • To address limitations of vertical stratification and mixed-pixel effects in dense orchard canopies.
  • To optimize spectral analysis for layer-specific nitrogen-spectral relationships.

Main Methods:

  • Co-registration of RGB 3D point clouds with hyperspectral orthomosaics for individual-tree localization.
  • Application of stratified sampling, continuous wavelet transform (CWT), and variable importance in projection (VIP)-based partial least squares regression (PLSR) for spectral optimization.
  • K-means clustering to isolate representative canopy pixels and assess mixed-pixel effects.
  • Field experiments conducted over two consecutive years (2023-2024).

Main Results:

  • Consistent CNC gradients observed, with lower canopy nitrogen exceeding upper canopy by 0.5-9.5% across treatments.
  • CWT-2 demonstrated the most accurate and robust performance for CNC estimation across years.
  • VIP-PLSR identified layer-dependent, CNC-informative wavelengths in visible, red-edge, and near-infrared regions.
  • Clustering-based pixel screening showed canopy-interior pixels achieved higher accuracy (R²val 0.69-0.76) than boundary-affected pixels (R²val 0.48-0.57).

Conclusions:

  • Coupling spectral feature optimization with layer-specific modeling and pixel screening significantly enhances tree-level CNC estimation accuracy in complex canopies.
  • The developed framework offers a mechanistic and operational basis for robust biochemical retrieval in structurally complex orchard systems.
  • This approach advances precision management strategies for high-density orchards.