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Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands
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UAV multi-source data fusion with super-resolution for accurate soybean leaf area index estimation.

Zhenqing Zhao1,2, Huabo Yao1,2, Depeng Zeng2,3

  • 1College of Electrical Engineering and Information, Northeast Agricultural University, Harbin, China.

Frontiers in Plant Science
|December 8, 2025
PubMed
Summary

Super-resolution (SR) image reconstruction combined with multi-sensor data improves Leaf Area Index (LAI) estimation in soybeans. This approach enhances accuracy despite varying Unmanned Aerial Vehicle (UAV) flight altitudes, benefiting precision agriculture.

Keywords:
UAV remote sensingleaf area indexmachine learningmulti-source data fusionsuper resolution

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Area of Science:

  • Agricultural Remote Sensing
  • Computer Vision
  • Biophysical Parameter Estimation

Background:

  • Leaf Area Index (LAI) is crucial for crop health assessment.
  • Unmanned Aerial Vehicles (UAVs) offer efficient crop monitoring but face altitude-related accuracy challenges.
  • Integrating super-resolution (SR) with multi-sensor data can potentially overcome these limitations.

Purpose of the Study:

  • To investigate the effectiveness of SR image reconstruction combined with multi-sensor data for soybean LAI estimation.
  • To evaluate the impact of different UAV flight altitudes on LAI estimation accuracy.
  • To compare various SR algorithms and data fusion strategies.

Main Methods:

  • Captured RGB and multispectral images at multiple UAV altitudes (15m-60m).
  • Applied SR algorithms (SwinIR, Real-ESRGAN, SRCNN, EDSR) for image enhancement.
  • Extracted texture features and developed LAI estimation models using XGBoost with data fusion (RGB, multispectral, combined).

Main Results:

  • SwinIR demonstrated superior SR performance; SR effectiveness decreased with altitude.
  • Fused RGB-multispectral data with XGBoost yielded the highest accuracy (4.16% relative error).
  • SR significantly improved accuracy at 30m (R²=0.86) and 45m (R²=0.77) altitudes.

Conclusions:

  • SR image reconstruction integrated with multi-sensor data effectively mitigates accuracy loss at higher UAV altitudes for LAI estimation.
  • This integrated approach offers a robust framework for precision agriculture and UAV-based crop monitoring.