基于无人机高光谱遥感的烤烟叶片叶绿素含量估测  

Monitoring of Leaf Chlorophyll Content in Flue-Cured Tobacco Based on Hyperspectral Remote Sensing of Unmanned Aerial Vehicle

作　　者：赖佳政 李贝贝 程翔 孙丰 陈炬廷 王晶 张芊 叶协锋 LAI Jiazheng;LI Beibei;CHENG Xiang;SUN Feng;CHENG Juting;WANG Jing;ZHANG Qian;YE Xiefeng(College of Tobacco Science,Henan Agricultural University/National Tobacco Cultivation and Physiology and Biochemistry Research Center/Key Laboratory for Tobacco Cultivation of Tobacco Industry,Zhengzhou 450002,China)

机构地区：[1]河南农业大学烟草学院/国家烟草栽培生理生化研究基地/烟草行业烟草栽培重点实验室,河南郑州450002

出　　处：《智慧农业（中英文）》2023年第2期68-81,共14页Smart Agriculture

基　　金：烟草行业烟草栽培重点实验室项目(30800665);河南省科技攻关项目(172102110168)。

摘　　要：[目的/意义]烤烟叶片叶绿素含量(Leaf Chlorophyll Content,LCC)是表征烤烟光合作用、营养状况和长势的重要指标。本研究的目的为高效精确地估测不同生长期烤烟LCC。[方法]以中烟100烟叶为研究对象,利用无人机搭载Resonon Pika L高光谱成像仪采集烤烟在6个关键生育期冠层反射率数据。基于相关分析筛选了21种LCC的敏感光谱指数,通过比较不同光谱组合及不同回归分析算法的预测精度,最终建立了基于多种光谱指数组合的LCC回归估测模型。采用一元线性回归(Unary Linear Regression,ULR)、多元线性回归(Multivariable Linear Regression,MLR)、偏最小二乘回归(Partial Least Squares Regression,PLSR)、支持向量回归(Support Vector Regression,SVR)和随机森林回归(Random Forest Regression,RFR) 5种建模方法进行LCC估测。[结果和讨论]在不同生育期大部分光谱参数与LCC的相关性达到极显著(P<0.01);相较于传统植被指数,新组合的光谱指数显著提升了与LCC的相关性;对单变量LCC估测模型ULR,以移栽后75 d新组合的归一化光谱指数与红光比率光谱指数的单变量建模精度最高,两者决定系数(Coefficient of Determination,R~2)和均方根误差(Root Mean Square Error,RMSE)分别为0.822和0.814,0.226和0.230。MLR、PLSR、SVR和RFR建模方法预测结果表明,RFR算法在LCC估测中效果最好,其中使用移栽后75 d数据验证集的R~2和RMSE可达0.919和0.146。[结论]本研究通过分析多种光谱指数与烤烟LCC的响应规律,构建可靠的烤烟叶片LCC估测模型,可为烤烟叶LCC估测以及烤烟的生长发育监测提供理论依据和技术支撑。[Objective] Leaf chlorophyll content(LCC) of flue-cured Tobacco is an important indicator for characterizing the photosynthesis,nutritional status,and growth of the crop.Tobacco is an important economic crop with leaves as the main harvest object,it is crucial to monitor its LCC.Hyperspectral data can be used for the rapid estimation of LCC in flue-cured tobacco leaves,making it of great significance and application value.The purpose of this study was to efficiently and accurately estimate the LCC of flue-cured tobacco during different growth stages.[Methods] Zhongyan 100 was chose as the research object,five nitrogen fertilization levels were set.In each plot,three plants were randomly and destructively sampled,resulting in a total of 45 ground samples for each data collection.After transplanting,the reflectance data of the flue-cured tobacco canopy at six growth stages(32,48,61,75,89,and 109 d) were collected using a UAV equipped with a Resonon Pika L hyperspectral.Spectral indices for the LCC estimation model of flue-cured tobacco were screened in two ways:(1) based on 18 published vegetation indices sensitive to LCC of crop leaves;(2) based on random combinations of any two bands in the wavelength range of 400-1000 nm.The Difference Spectral Index(DSI),Ratio Spectral Index(RSI),and Normalized Spectral Index(NDSI) were calculated and plotted against LCC.The correlations between the three spectral indices and leaf LCC were calculated and plotted using contour maps.Five regression models,unary linear regression(ULR),multivariable linear regression(MLR),partial least squares regression(PLSR),support vector regression(SVR),and random forest regression(RFR),were used to estimate the chlorophyll content.A regression estimate model of LCC based on various combinations of spectral indices was eventually constructed by comparing the prediction accuracies of single spectral index models multiple spectral index models at different growth stages.[Results and Discussions] The results showed that the LCC range for six growt

关 键 词：烤烟 叶绿素含量估测 无人机 光谱参数 随机森林回归 多元线性回归 偏最小二乘回归 支持向量机回归 

分 类 号：S572[农业科学—烟草工业] S127[农业科学—作物学]