基于无人机影像的冬小麦株高提取与LAI估测模型构建  

Plant Height Extraction of Winter Wheat and Construction of LAI Estimation Model Based on UAV Images

作　　者：夏积德[1] 牟湘宁 张鑫 张怡宁[2] 梁琼丹 张青峰[2] 王稳江[1] XIA Jide;MOU Xiangning;ZHANG Xin;ZHANG Yining;LIANG Qiongdan;ZHANG Qingfeng;WANG Wenjiang(Yangling Vocational and Technical College,Yangling,Shaanxi 712100,China;Northwest A&F University,Yangling,Shaanxi 712100,China)

机构地区：[1]杨凌职业技术学院,陕西杨凌712100 [2]西北农林科技大学,陕西杨凌712100

出　　处：《陕西农业科学》2024年第6期77-84,共8页Shaanxi Journal of Agricultural Sciences

基　　金：杨凌职业技术学院基金项目(ZK21-42);陕西省重点研发计划项目(2023-YBNY-042);陕西省农业农村厅科技创新驱动项目(NYKJ-2021-ST);教育部高校学生司项目(20230104253);杨凌职业技术学院基金项目(JG2021002);陕西职业教育改革研究课题(2024SZX29)。

摘　　要：株高和叶面积指数(Leaf Area Index,LAI)反映着作物的生长发育状况。为了探究基于无人机可见光遥感提取冬小麦株高的可靠性,以及利用株高和可见光植被指数估算LAI的精度,本文获取了拔节期、抽穗期、灌浆期的无人机影像,提取了冬小麦株高与可见光植被指数,使用逐步回归、偏最小二乘、随机森林、人工神经网络四种方法建立LAI估测模型,并对株高提取及LAI估测情况进行精度评价。结果显示:(1)株高提取值Hc与实测值Hd高度拟合(R^(2)=0.894,RMSE=6.695,NRMSE=9.63%),株高提取效果好;(2)与仅用可见光植被指数相比,基于株高与可见光植被指数构建的LAI估测模型精度更高,且随机森林为最优建模方法,当其决策树个数为50时模型估测效果最好(R^(2)=0.809,RMSE=0.497,NRMSE=13.85%,RPD=2.336)。利用无人机可见光遥感方法,高效、准确、无损地实现冬小麦株高及LAI提取估测可行性较高,该研究结果可为农情遥感监测提供参考。Plant height and leaf area index(LAI)are important agronomic traits of crops that reflect their growth and development to some extent.In this study,we aimed to explore the reliability of winter wheat plant height extraction based on UAV visible remote sensing and assess the accuracy of LAI estimation using plant height and visible vegetation index.We obtained the plant height and visible vegetation index,and used four modeling methods(stepwise regression,partial least squares,random forest,and artificial neural network)to establish LAI estimation models.Subsequently,we evaluated the accuracy of plant height extraction and LAI estimation.The results showed that:(1)Extracted plant height Hc was well-fitted with measured plant height Hd(R^(2)=0.894,RMSE=6.695,NRMSE=9.63%),indicating good performance in plant height extraction;(2)In contrast to use of the visible light vegetation index only,the LAI estimation model constructed based on plant height and visible light vegetation index had higher accuracy.In addition,the random forest was the optimal modeling method,when the number of decision trees was 50,the model estimated the best effect(R^(2)=0.809,RMSE=0.497,NRMSE=13.85%,RPD=2.336).Using UAV visible light remote sensing method,it has a high possibility to extract the plant height and estimate the LAI of winter wheat efficiently,accurately and without damage.The results can provide a reference for remote sensing-based monitoring of agricultural conditions.

关 键 词：无人机可见光遥感 冬小麦 株高 叶面积指数 估测模型 

分 类 号：S512[农业科学—作物学]