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Light Acquisition

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Related Experiment Video

Updated: Jan 11, 2026

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

Improving sugar beet canopy mapping through UAV image analysis.

Jianjun Jiang^1,2,3, Donghui Li⁴, Qiansheng Qiu³

¹School of Electrical Automation and Information Engineering, Tianjin University, Tianjin, 300072, China.

Scientific Reports

|November 17, 2025

Summary

This summary is machine-generated.

Unmanned aerial vehicles (UAVs) with RGB cameras can accurately estimate sugar beet fractional vegetation cover (FVC). The Excess Green (ExG) index combined with Otsu or Ridler-Calvard (RC) thresholding showed the highest accuracy for FVC estimation.

Keywords:

Crop monitoring Fractional vegetation cover Image segmentation Sugar beet UAV imagery

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Area of Science:

Agricultural Remote Sensing
Precision Agriculture
Computer Vision

Background:

Fractional Vegetation Cover (FVC) is crucial for monitoring crop health and vegetation modeling.
Unmanned Aerial Vehicles (UAVs) offer a cost-effective alternative for FVC estimation compared to traditional methods.

Purpose of the Study:

To evaluate 18 segmentation methods for estimating sugar beet FVC using UAV RGB imagery.
To identify the most accurate vegetation indices and thresholding algorithms for FVC assessment.

Main Methods:

Applied six vegetation indices (e.g., ExG, GLI, RGBVI) combined with three thresholding algorithms (Otsu, Ridler-Calvard, Two-Peaks).
Validated methods against ground truth data from 30 sugar beet plots across four growth stages.
Analyzed accuracy using Normalized Root Mean Square Error (NRMSE) and coefficient of determination (R²).

Main Results:

Excess Green (ExG) index with Otsu and Ridler-Calvard (RC) thresholding yielded the highest accuracy (NRMSE = 5.1%, R² = 0.96).
ExG-based methods showed superior correlation with field measurements.
ExGB with Two-Peaks method performed poorly (NRMSE = 42.3%, R² = 0.34).

Conclusions:

ExG combined with Otsu or RC thresholding is a promising approach for UAV-based sugar beet FVC estimation.
Further validation across diverse conditions is recommended to confirm the robustness of these methods.