关键词: 烟草品质, 无人机, 多源遥感, 特征融合, 机器学习, 无损监测

Abstract: To achieve rapid and non⁃destructive monitoring of chemical quality indicators in tobacco，this study was conducted at the Tangyin Tobacco Station in Fuzhou City，Jiangxi Province，using two tobacco varieties，Yunyan 87 and ZY157，under five nitrogen application levels（0，105.0，127.5，157.5，and 180. 0 kg/ha）. During key growth stages，multispectral and RGB images along with three⁃dimensional point cloud data were acquired using DJI Phantom 4 and Mavic 3T drones. Spectral vegetation indices，color indices，texture features，and three⁃dimensional structural features were extracted from the data.Estimation models for key chemical components such as nicotine，nitrogen，and potassium were constructed using random forest（RF），least absolute shrinkage and selection operator（LASSO），and partial least square regression（PLSR）algorithms. The results indicated that all feature types exhibited strong responsiveness to tobacco quality，with the red⁃edge optimized soil⁃adjusted vegetation index（REOSAVI） showing the higher correlation with nicotine，nitrogen，and potassium contents（|r| ranged from 0.68 to 0.86）. Compared to single⁃feature modeling，multi⁃source feature fusion modeling significantly improved prediction accuracy. For nitrogen content monitoring，the texture feature combination performed best（R²=0. 89），while for nicotine and potassium content prediction，the fusion of all four feature types achieved optimal results，with R² values of 0.88—0.89 and RMSE values of 0.22%—0.31%. Algorithm comparison revealed that RF and LASSO outperformed PLSR in both fitting and predictive capabilities，with LASSO demonstrating the best performance in multi⁃feature fusion modeling. Spatial distribution maps of quality parameters generated based on the optimal model clearly reflected the spatial variability of tobacco quality under different nitrogen application levels. In conclusion，integrating multi⁃source UAV remote sensing features with machine learning algorithms effectively enhances the estimation accuracy of tobacco chemical quality indicators，providing technical support for tobacco quality monitoring，precision fertilization，and intelligent field management.

Key words: Tobacco quality, Unmanned aerial vehicle（UAV）, Multi?source remote sensing, Feature fusion, Machine learning, Non?destructive monitoring