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基于无人机多源数据的花生表型估算模型

HE Ning WANG Jian LU Xianju CHEN Bo BAI Bo FAN Jiangchuan

农业机械学报2026,Vol.57Issue(1):114-124,11.
农业机械学报2026,Vol.57Issue(1):114-124,11.DOI:10.6041/j.issn.1000-1298.2026.01.011

基于无人机多源数据的花生表型估算模型

Peanut Phenotype Estimation Model Based on Multi-source Data from Unmanned Aerial Vehicles

HE Ning 1WANG Jian 2LU Xianju 1CHEN Bo 1BAI Bo 3FAN Jiangchuan1

作者信息

  • 1. Information Technology Research Center,Beijing Academy of Agriculture and Forestry Sciences,Beijing 100097,China||Beijing Key Laboratory of Digital Plant,Beijing 100097,China
  • 2. Beijing Digital Agriculture Rural Promotion Center,Beijing 101117,China
  • 3. Institute of Crop Germplasm Resources,Shandong Academy of Agricultural Sciences,Ji'nan 250100,China||Shandong Provincial Key Laboratory of Crop Genetic Improvement and Ecological Physiology,Ji'nan 250100,China
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摘要

Abstract

Peanut(Arachis hypogaea L.),a critical oilseed crop,plays a crucial role in ensuring food and oil production security.Accurate,nondestructive,and real-time phenotypic monitoring is essential for optimizing peanut production management.Multispectral data acquired by an unmanned aerial vehicle(UAV)platform during key growth stages were leveraged to extract canopy multispectral(MS),structural(CHM),and textural(TEX)parameters.Four machine learning algorithms,partial least squares regression(PLSR),support vector machine(SVM),artificial neural network(ANN),and random forest regression(RFR),were employed to construct estimation models for plant height,SPAD values,and aboveground biomass.Results demonstrated strong correlations between peanut aboveground biomass/plant height and the near-infrared band(Pearson correlation coefficients were 0.77 and 0.69,respectively).The random forest model,integrating textural,structural,and spectral features,achieved optimal biomass estimation accuracy(R2=0.96).For plant height inversion,the PLSR model combining textural and spectral features performed best(R2=0.94).SPAD estimation using PLSR with fused textural and structural features yielded moderate accuracy(R2=0.39,RMSE=3.06,nRMSE=0.062,RPD=1.30).The research identified feature-specific requirements for machine learning-based estimation of distinct peanut phenotypic traits and established a UAV multi-source data fusion framework capable of accurate,nondestructive,and efficient assessment of plant height and biomass.These findings can provide a robust technical approach for growth monitoring and precision management in peanut cultivation systems.

关键词

花生/表型性状估算模型/多源数据融合/机器学习/无人机遥感

Key words

peanut/phenotypic trait estimation model/multi-source data fusion/machine learning/UAV remote sensing

分类

农业科技

引用本文复制引用

HE Ning,WANG Jian,LU Xianju,CHEN Bo,BAI Bo,FAN Jiangchuan..基于无人机多源数据的花生表型估算模型[J].农业机械学报,2026,57(1):114-124,11.

基金项目

山东省重点研发计划项目(2021LZGC026)和北京市农林科学院作物表型组学协同创新中心项目(KJCX20240406) (2021LZGC026)

农业机械学报

1000-1298

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