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Improved Multi-Stage Rice Above-Ground Biomass Estimation Using Wavelet-Texture-Fused Vegetation Indices from UAV

Jinpeng Li1,2, Qiang Cao1,2, Shuaipeng Wang1,2

  • 1College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China.

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|September 27, 2025
PubMed
Summary
This summary is machine-generated.

Discrete wavelet transform textures (WTs) improve rice above-ground biomass (AGB) estimation, especially when combined with vegetation indices (VIs). This integrated approach offers a more accurate and stable method for monitoring AGB across growth stages.

Keywords:
AGBUAVdiscrete wavelet transform (DWT)multi-feature fusionplant phenotypingrice

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Area of Science:

  • Agricultural Remote Sensing
  • Biomass Estimation
  • Signal Processing

Background:

  • Traditional vegetation indices (VIs) for estimating above-ground biomass (AGB) saturate under dense canopies and poorly detect vertical growth.
  • Discrete Wavelet Transform (DWT) offers potential for extracting canopy structure features but is underexplored for crop biomass monitoring.

Purpose of the Study:

  • To evaluate Discrete Wavelet Transform (DWT)-based textures for estimating rice AGB across multiple growth stages.
  • To assess if combining DWT-based texture indices (WTIs) with VIs enhances AGB estimation accuracy.

Main Methods:

  • Field experiments with four rice varieties and five nitrogen treatments under UAV multispectral imaging.
  • Extraction of VIs and wavelet textures (WTs), construction of novel WTIs.
  • Development of AGB estimation models using correlation analysis, regression, and random forest (RF).

Main Results:

  • Wavelet texture indices (WTIs) showed higher correlation with rice AGB than single WTs.
  • The RF model demonstrated superior performance; WTIs excelled in post-heading and all-stage AGB estimation.
  • Combining WTIs with VIs significantly improved AGB prediction accuracy (R² increased by 10-15%) and reduced errors (RMSE decreased by 13-17%) compared to VIs alone.

Conclusions:

  • DWT-based textures effectively enhance high-precision rice AGB estimation.
  • Integrating WTIs with VIs provides an economical, efficient, accurate, and stable method for rice AGB prediction across diverse conditions.