农业机械学报2026，Vol.57Issue(17)：104-114,11.DOI:10.6041/j.issn.1000-1298.2026.17.010
基于无人机LiDAR和RGB多特征数据融合的玉米株高估测研究
Research on Maize Plant Height Estimation Based on UAV LiDAR and RGB Multi-feature Data Fusion
摘要
Abstract
Maize plant height is a key factor for estimating biomass and predicting yield,and it is also an important reference for maize growth management.UAV remote sensing data of maize seedling stage,jointing stage,tasseling stage,filling stage and mature stage were selected to explore the influence of filtering algorithm and spatial resolution on the construction of digital elevation model(DEM).The advantages of LiDAR and RGB data in maize plant height estimation were explored.By combining the advantages of the two data,a regression model based on LiDAR and RGB multi-feature data fusion was constructed.The DEM with a spatial resolution of 0.25 m obtained based on the improved progressive triangulation encryption filtering algorithm had the highest accuracy,with a determination coefficient(R2)of 0.944 1,a root mean square error(RMSE)of 0.063 m,and a normalized root mean square error(NRMSE)of 0.151％.The linear regression(LR)model constructed by fusing the 100th percentile of LiDAR and RGB crop height model(CHM),visible light atmospheric resistance index(VARI),red,green and blue vegetation index(RGBVI)and canopy volume(CV)had the highest accuracy,R2=0.986 4,RMSE=0.088 m,NRMSE=6.331％.Compared with the optimal single feature linear regression model,R2 was increased by 0.5％,RMSE and NRMSE was decreased by 16.2％.By establishing a regression model that integrated LiDAR and RGB multi-feature data,the accuracy of plant height estimation was improved.The research result can provide a technical method for accurately estimating the plant height of maize in the whole growth period.关键词
玉米/株高/无人机遥感/数字高程模型/作物高度模型/多特征数据融合Key words
maize/plant height/UAV remote sensing/digital elevation model/crop height model/multi-feature data fusion分类
农业科技引用本文复制引用
徐良骥,王国辉,陈永春,张坤,程海燕,苗伟,黄刚..基于无人机LiDAR和RGB多特征数据融合的玉米株高估测研究[J].农业机械学报,2026,57(17):104-114,11.基金项目
深部煤炭安全开采与环境保护全国重点实验室开放基金项目(2025YB018)、国家自然科学基金面上项目(52574211)和安徽省高等学校科学研究项目(2023AH051208) （2025YB018）
