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Utilizing Spectral, Structural and Textural Features for Estimating Oat Above-Ground Biomass Using UAV-Based

Rakshya Dhakal1, Maitiniyazi Maimaitijiang2, Jiyul Chang3

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Unmanned Aerial Vehicles (UAVs) combined with machine learning improve oat biomass estimation by integrating spectral, structural, and textural data. This advanced phenotyping enhances crop breeding efficiency.

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Area of Science:

  • Agricultural Science
  • Remote Sensing
  • Plant Breeding

Background:

  • Accurate above-ground biomass monitoring is crucial for plant breeding, but traditional methods are labor-intensive and costly.
  • Unmanned Aerial Vehicles (UAVs) provide a rapid, non-destructive phenotyping solution for field plots.
  • Existing Vegetation Index (VI) methods primarily use spectral data, neglecting 3D canopy structure and spatial relationships.

Purpose of the Study:

  • To explore the integration of UAV multispectral imagery-derived spectral, structural, and textural features with machine learning for accurate oat biomass estimation.
  • To assess the importance of canopy structural and textural features alongside spectral features.
  • To compare the predictive performance of different machine learning algorithms for biomass estimation.

Main Methods:

  • UAV multispectral imagery was acquired for six oat genotypes across two locations and multiple growth stages in 2020 and 2021.
  • Plot-level canopy spectral, structural, and textural features were extracted from the imagery.
  • Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Random Forest Regression (RFR) were employed to estimate biomass.

Main Results:

  • Canopy structural and textural features were identified as important indicators for oat biomass estimation, complementing spectral data.
  • Combining spectral, structural, and textural features significantly enhanced biomass estimation accuracy compared to using single feature types.
  • Machine learning algorithms demonstrated strong predictive ability, with Random Forest Regression (RFR) achieving the highest accuracy (R² = 0.926, RMSE% = 15.97%).

Conclusions:

  • UAV-based multi-feature fusion with machine learning offers a promising approach for accurate above-ground biomass estimation in oat breeding nurseries.
  • This integrated method can significantly improve the efficiency of oat breeding programs through advanced phenotyping.
  • The findings support the adoption of UAV-based phenotyping for enhanced crop management practices.