烟草科技2025，Vol.58Issue(5)：96-105,10.
基于无人机高光谱影像的烤烟上部叶烟碱含量监测
Monitoring nicotine content in upper leaves of flue-cured tobacco based on UAV hyperspectral images
摘要
Abstract
For fast,non-destructively and real-time monitoring of nicotine content in upper leaves of flue-cured tobacco in the field,experiments were conducted with two tobacco cultivars(Cuibi No.1 and Yunyan 87)at five fertilizer levels across different tobacco growing regions.A UAV remote sensing platform equipped with a hyperspectral camera was used to acquire canopy hyperspectral data at three post topping growth stages(topping stage and mature stages)on lower and middle leaves.The original canopy spectra were preprocessed by single or combined preprocessing methods,and the characteristic bands for nicotine in upper leaves across the three growth stages were screened by successive projection algorithm(SPA)and random frog algorithm(RF).Partial least squares regression(PLSR),support vector machine(SVM),ridge regression(RR),kernel ridge regression(KRR)and gradient boosting regression tree(GBR)models were established using both all spectra bands and characteristic bands to predict nicotine content in upper leaves across the three growth stages.The results showed that:1)Appropriate preprocessing methods improved model accuracy for nicotine content in upper leaves across the growth stages,and the first derivative combined with the standard normal variate(D1-SNV)showed optimal prediction performance for nicotine content in upper leaves across the three stages.2)RF and SPA were able to screen out 19 and 13 characteristic bands respectively.Of these two techniques,the characteristic bands screened by RF were relatively more evenly distributed and included the visible and near infrared bands,while the characteristic bands screened by SPA were less evenly distributed and the number of characteristic bands in the 400-700 nm range was lower.3)In general,the D1-SNV-RF-KRR model achieved optimal prediction performance with R2,RMSE and RPD of the test set of 0.872,0.228 and 2.796 respectively,indicating higher accuracy and stability.This study provides a methodological foundation for large-scale real-time monitoring of nicotine content in upper leaves of flue-cured tobacco using UAV-based hyperspectral remote sensing technology after topping.关键词
烤烟/上部叶/烟碱/遥感/高光谱/随机蛙跳算法/核岭回归Key words
Flue-cured tobacco/Upper leaf/Nicotine/Remote sensing/Hyperspectrum/Random leapfrog algorithm/Kernel ridge regression分类
农业科学引用本文复制引用
廖杨子杰,梁太波,陈星峰,过伟民,徐嫱,李博洋,张艳玲..基于无人机高光谱影像的烤烟上部叶烟碱含量监测[J].烟草科技,2025,58(5):96-105,10.基金项目
中国烟草总公司重点研发项目"品牌导向型高可用性上部烟叶开发与高效利用技术研究"(110202102035) （110202102035）
中国烟草总公司福建省公司科技项目"福建烟叶质量大数据系统平台研究与应用"(2023350000200078). （2023350000200078）
