数字农科院2.0

Estimation of Leaf, Spike, Stem and Total Biomass of Winter Wheat Under Water-Deficit Conditions Using UAV Multimodal Data and Machine Learning

文献类型： 外文期刊

作者： Jinhang Liu;Wenying Zhang;Yongfeng Wu;Juncheng Ma;Yulin Zhang;Binhui Liu

作者机构：

关键词： aboveground biomass;machine learning;multimodality;UAV;winter wheat

期刊名称： Remote Sensing

ISSN： 2072-4292

年卷期： 2025 年 17 卷 15 期

页码：

收录情况： SCIE（2025版） ; ; EI（2025版）

摘要： Accurate estimation aboveground biomass (AGB) in winter wheat is crucial for yield assessment but remains challenging to achieve non-destructively. Unmanned aerial vehicle (UAV)-based remote sensing offers a promising solution at the plot level. Traditional field sampling methods, such as random plant selection or full-quadrat harvesting, are labor intensive and may introduce substantial errors compared to the canopy-level estimates obtained from UAV imagery. This study proposes a novel method using Fractional Vegetation Coverage (FVC) to adjust field-sampled AGB to per-plant biomass, enhancing the accuracy of AGB estimation using UAV imagery. Correlation analysis and Variance Inflation Factor (VIF) were employed for feature selection, and estimation models for leaf, spike, stem, and total AGB were constructed using Random Forest (RF), Support Vector Machine (SVM), and Neural Network (NN) models. The aim was to evaluate the performance of multimodal data in estimating winter wheat leaves, spikes, stems, and total AGB. Results demonstrated that (1) FVC-adjusted per-plant biomass significantly improved correlations with most indicators, particularly during the filling stage, when the correlation between leaf biomass and NDVI increased by 56.1%; (2) RF and NN models outperformed SVM, with the optimal accuracies being R2 = 0.709, RMSE = 0.114 g for RF, R2 = 0.66, RMSE = 0.08 g for NN, and R2 = 0.557, RMSE = 0.117 g for SVM. Notably, the RF model achieved the highest prediction accuracy for leaf biomass during the flowering stage (R2 = 0.709, RMSE = 0.114); (3) among different water treatments, the R2 values of water and drought treatments were higher 0.723 and 0.742, respectively, indicating strong adaptability. This study provides an economically effective method for monitoring winter wheat growth in the field, contributing to improved agricultural productivity and fertilization management.

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