可见光波段无人机遥感图像的小麦茎蘖密度定量反演  

Inversion of Wheat Tiller Density Based on Visible-Band Images of Drone

作　　者：杜蒙蒙 Ali Roshanianfard 刘颖超 DU Meng-meng;Ali Roshanianfard;LIU Ying-chao(School of Agricultural Equipment Engineering,Henan University of Science and Technology,Luoyang 471003,China;Faculty of Agriculture and Natural Resources,University of Mohaghegh Ardabili,56199-11367,Ardabil,Iran;School of Mechanical Engineering,Shanghai Jiao Tong University,Shanghai 200240,China)

机构地区：[1]河南科技大学农业装备工程学院,河南洛阳471003 [2]Faculty of Agriculture and Natural Resources,University of Mohaghegh Ardabili,56199-11367,Ardabil,Iran [3]上海交通大学机械与动力工程学院,上海200240

出　　处：《光谱学与光谱分析》2021年第12期3828-3836,共9页Spectroscopy and Spectral Analysis

基　　金：国家重点研发计划项目(2019YFE0125500);河南省高等学校重点科研计划项目(20A416001)资助。

摘　　要：在小麦分蘖期内,适时适量追施氮肥可显著改善小麦茎蘖群体结构、提高产量。但经验性的均一施肥往往导致氮肥过度施用及农学效率偏低等问题,而基于小麦茎蘖的实际发育状况进行变量施肥,有助于解决小麦茎蘖个体发育与群体结构之间的矛盾。通过变量追施氮肥作业调控小麦茎蘖群体、提高小麦产量的技术关键,在于准确获取田块尺度的小麦茎蘖密度(单位面积内的小麦茎蘖数量)信息。传统的通过人工田间调查获取小麦茎蘖密度信息的方法,时效性与精准度不足,工作量大、效率低,而且稀疏的点源统计数据无法精准反映田块内部的小麦茎蘖密度空间差异状况。因此,为满足变量追施氮肥作业对田块尺度的小麦茎蘖密度专题图的需求,使用大疆Mini 2航拍无人机,在小麦分蘖期获取试验田的可视光波段遥感图像。使用Matlab相机标定工具箱,完成无人机遥感图像校正,提取蓝、绿、红三个可视光波段的图像分量。基于植被与土壤在可见光波段的光谱响应特性,选取可以较好地突出植被特征、减轻光照强度对遥感图像质量造成影响的4种比值类型植被指数,即可见光波段差分植被指数(VDVI)、归一化绿红差分指数(NGRDI)、归一化绿蓝差分指数(NGBDI)、绿红比值指数(RGRI)。在此基础上,利用VDVI专题图,计算小麦试验田的植被覆盖度(FVC)。进一步以FVC,VDVI,NGRDI,NGBDI及RGRI平均值为5节点输入层,小麦茎蘖密度地面真值为单节点输出层,建立一个单隐含层、5输入、单输出的3层BP神经网络预测模型,用以定量反演小麦茎蘖密度指标。精度验证数据表明:该神经网络模型的预测结果与相应的小麦茎蘖密度地面真值之间的均方根误差(RMSE)及平均绝对百分比误差(MAPE)分别为19及3.62%,因此该模型具有较高的小麦茎蘖密度预测精度。田块尺度的小麦茎蘖密度反演专题图的统计数据显示Nitrogen topdressing is a vital agrotechnical measure to boost tillering process and improve the population structure of wheat stalks.However,uniform nitrogen topdressing is apt to cause excessive application and low agronomic efficiency of nitrogen fertilizer.However,variable-rate fertilization can solve the contradiction between individual development and formation of population structure of wheat stalks,by decreasing the application of nitrogen fertilizer according to the actual growing status of wheat stalks in the field.The key technology to improve the population structure of wheat stalks using variable-rate nitrogen topdressing is to accurately obtain the information of spatial variations of wheat stalk numbers at field scale.Thus,with the objectives of fertilizer reduction and yield increasing,this research studies wheat growth status in the tillering stage to invert and regulate population densities of wheat stalks at field scale.Firstly,a DJI mini 2 with a CMOS(Complementary Metal Oxide Semiconductors)image sensor is utilized to acquire visible-band imagery of wheat from the experimental field.Secondly,vegetation indices of ratio type such asVDVI(Visible-band Difference Vegetation Index),NGRDI(Normalized Green-Red Difference Index),NGBDI(Normalized Green-Blue Difference Index),and RGRI(Ratio Green-Red Index)were calculated out of the visible-band images,in order to highlight vegetation features and reduce the impact of uneven light intensity on remote sensing images.Furthermore,FVC(Fractional Vegetation Coverage),which indicates the growth vigor of both individuals of wheat tillers and stalk population as a whole,was calculated based on the VDVI map.Subsequently,a BP(Backward Propagation)Neural Network prediction model was built to quantitatively invert wheat stalk density,using FGV,VDVI,NGRDI,NGBDI,and RGRI the input layer,and ground truth samples of wheat stalk densities as output layer.Upon completion of the BP Neural Network training,weight and threshold values of the prediction model were obtained,

关 键 词：无人机 农业遥感 小麦长势 小麦分蘖 植被指数 植被覆盖度 

分 类 号：S237[农业科学—农业机械化工程]