您好，欢迎访问河北省农林科学院 机构知识库！
河北省农林科学院机构知识库

全部

  • 全部
  • 标题
  • 学者
  • 机构
  • 关键词

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： Liu, Jinhang 1 ; Zhang, Wenying 2 ; Wu, Yongfeng 1 ; Ma, Juncheng 4 ; Zhang, Yulin 1 ; Liu, Binhui 2 ;

作者机构： 1.Chinese Acad Agr Sci, Inst Environm & Sustainable Dev Agr, Beijing 100081, Peoples R China

2.Hebei Acad Agr & Forestry Sci, Dryland Farming Inst, Hengshui 053000, Peoples R China

3.Key Lab Crop Drought Tolerance Res Hebei Prov, Hengshui 053000, Peoples R China

4.China Agr Univ, Coll Water Resources & Civil Engn, Beijing 100083, Peoples R China

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

期刊名称：REMOTE SENSING （ 影响因子：4.1； 五年影响因子：4.8 ）

ISSN：

年卷期： 2025 年 17 卷 15 期

页码：

收录情况： SCI

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.

  • 相关文献
作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：河北省农林科学院

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台