基于无人机LiDAR和RGB多特征数据融合的玉米株高估测研究OA

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.