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Purpose <p>Above-ground biomass (AGB) is a critical indicator for assessing the growth status of winter wheat. Since the range of extracted color indices (CIs) tends to remain constant after flowering and UAV RGB images cannot capture the lower and middle structures of the canopy under dense planting conditions, the efficiency of AGB estimation models is limited. Therefore, this study aimed to improve the accuracy of winter wheat AGB estimation by incorporating canopy volume information with RGB-based CIs.</p> Methods <p>RGB images were acquired to generate Digital Orthophoto Maps (DOM) and Digital Surface Models (DSM) at Feekes 10, Feekes 10.5.2, Feekes 10.5.4, and Feekes 11.3 growth stages. Eight biomass-related CIs were extracted from the DOM, and canopy volume (V) was calculated from the DSM for corresponding regions. The RReliefF algorithm was applied to rank feature importance and select optimal features. Eight statistical and machine learning regression algorithms, including Gaussian process regression (GPR), were used to construct AGB estimation models with different feature combinations.</p> Results <p>The results showed that the GPR algorithm outperformed other regression methods, achieving the highest estimation accuracy with R² values of 0.775, 0.741, 0.702, and 0.568 at the four growth stages, respectively. Compared with models using CIs alone, integrating canopy volume with CIs improved AGB estimation accuracy from Feekes 10.5.2 to Feekes 11.3, with R² increases of 7.31%, 6.55%, and 22.98%, respectively.</p> Conclusion <p> Overall, combining canopy volume features derived from UAV-based RGB imagery with CIs and applying effective machine learning algorithms enables rapid and accurate estimation of winter wheat AGB.</p>

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Wheat biomass estimation by fusing color index and canopy volume based on UAV RGB images

  • 期刊论文

  • 发表日期：

    2026年2月9日

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  • Zhaosheng Yao,
  • Dongwei Han,
  • Ruimin Shao,
  • Hainie Zha,
  • Shaolong Zhu,
  • Jianliang Wang,
  • Muhammad Zain,
  • Tao Liu,
  • Fei Wu,
  • Yuanzhi Wang,
  • Chengming Sun

摘要

Purpose

Above-ground biomass (AGB) is a critical indicator for assessing the growth status of winter wheat. Since the range of extracted color indices (CIs) tends to remain constant after flowering and UAV RGB images cannot capture the lower and middle structures of the canopy under dense planting conditions, the efficiency of AGB estimation models is limited. Therefore, this study aimed to improve the accuracy of winter wheat AGB estimation by incorporating canopy volume information with RGB-based CIs.

Methods

RGB images were acquired to generate Digital Orthophoto Maps (DOM) and Digital Surface Models (DSM) at Feekes 10, Feekes 10.5.2, Feekes 10.5.4, and Feekes 11.3 growth stages. Eight biomass-related CIs were extracted from the DOM, and canopy volume (V) was calculated from the DSM for corresponding regions. The RReliefF algorithm was applied to rank feature importance and select optimal features. Eight statistical and machine learning regression algorithms, including Gaussian process regression (GPR), were used to construct AGB estimation models with different feature combinations.

Results

The results showed that the GPR algorithm outperformed other regression methods, achieving the highest estimation accuracy with R² values of 0.775, 0.741, 0.702, and 0.568 at the four growth stages, respectively. Compared with models using CIs alone, integrating canopy volume with CIs improved AGB estimation accuracy from Feekes 10.5.2 to Feekes 11.3, with R² increases of 7.31%, 6.55%, and 22.98%, respectively.

Conclusion

Overall, combining canopy volume features derived from UAV-based RGB imagery with CIs and applying effective machine learning algorithms enables rapid and accurate estimation of winter wheat AGB.