您好，欢迎访问中国农业科学院 机构知识库！

全部

  • 全部
  • 标题
  • 学者
  • 机构
  • 关键词

Maize biomass estimation by integrating spectral, structural, and textural features from unmanned aerial vehicle data

文献类型： 外文期刊

作者： Meng, Lin 1 ; Ming, Bo 2 ; Liu, Yuan 4 ; Nie, Chenwei 2 ; Fang, Liang 2 ; Zhou, Lili 2 ; Xin, Jiangfeng 2 ; Xue, Beibei 2 ; Liang, Zhongyu 2 ; Guo, Huirong 2 ; Yin, Dameng 1 ; Jin, Xiuliang 1 ;

作者机构： 1.Chinese Acad Agr Sci, Natl Nanfan Res Inst Sanya, Sanya 572024, Peoples R China

2.Chinese Acad Agr Sci, Inst Crop Sci, Beijing 100081, Peoples R China

3.Chinese Acad Agr Sci, Agr Informat Inst, Beijing 100081, Peoples R China

4.Ningbo Alatu Digital Technol Co Ltd, Ningbo 315000, Peoples R China

5.Zhejiang Univ Water Resources & Elect Power, Sch Geospatial Sci & Engn, Hangzhou 310018, Peoples R China

关键词： Unmanned Aerial Vehicle (UAV); Aboveground biomass (AGB); Organ biomass; Maize; Ensemble learning

期刊名称：EUROPEAN JOURNAL OF AGRONOMY （ 影响因子：5.5； 五年影响因子：5.9 ）

ISSN： 1161-0301

年卷期： 2025 年 168 卷

页码：

收录情况： SCI

摘要： The rapid and accurate estimation of maize aboveground biomass (AGB) and organ biomass at the field scale is crucial for monitoring crop growth and predicting yield. However, there is limited research on estimating crop organ biomass from unmanned aerial vehicle (UAV) remote sensing. This study used a multispectral (MS) camera and LiDAR sensor to acquire data at various maize growth stages across two experimental regions. The variations in maize organ biomass throughout the growing season were analyzed. Vegetation indices (VIs), canopy structure features (SFs), and texture features (TFs) were combined to create five different datasets and fed into two ensemble learning methods, i.e., Random Forest Regression (RFR) and XGBoost Regression (XGBR), to estimate maize AGB and organ biomass. The results indicated that: (i) Leaf and stalk biomass almost ceased to change after the tasseling stage. Stalk and ear biomass, compared to leaf biomass, are more strongly correlated with AGB. (ii) AGB estimation was improved by incorporating more indicators into the ensemble learning model, with the RFR model with all indicators achieving the best estimation accuracy (R2 = 0.917, RMSE = 189.664 g/m2, rRMSE = 21.2 %, MAE = 124.617 g/m2). (iii) Leaf and ear biomass estimation was comparable using models inputting all indicators or inputting VIs+TFs, suggesting that MS data were significant for leaf and ear biomass estimation, while SFs played an important role in stalk biomass estimation. This study accurately estimated organ-level maize biomass and AGB by combining different types of UAV remote sensing indicators and machine learning, which provides a valuable reference for organ biomass estimation of other crop types and related precision agriculture studies.

  • 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：中国农业科学院

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