群体小麦条锈病发病动态无人机遥感监测方法  

Analysis for stripe rust dynamics in wheat population using UAV remote sensing

作　　者：苏宝峰[1,2,3] 刘昱麟 黄彦川 蔚睿 曹晓峰 韩德俊 Su Baofeng;Liu Yulin;Huang Yanchuan;Yu Rui;Cao Xiaofeng;Han Dejun(College of Mechanical and Electronic Engineering,Northwest A&F University,Yangling 712100,China;Key Laboratory of Agricultural Internet of Things,Ministry of Agriculture and Rural Affairs,Yangling 712100,China;Shaanxi Key Laboratory of Agriculture Information Perception and Intelligent Service,Yangling 712100,China;College of Agronomy,Northwest A&F University,Yangling 712100,China;State Key Laboratory of Crop Stress Biology in Arid Areas,Northwest A&F University,Yangling 712100,China)

机构地区：[1]西北农林科技大学机械与电子工程学院,杨凌712100 [2]农业农村部农业物联网重点实验室,杨凌712100 [3]陕西省农业信息感知与智能服务重点实验室 [4]西北农林科技大学农学院,杨凌712100 [5]西北农林科技大学旱区作物逆境生物学国家重点实验室,杨凌712100

出　　处：《农业工程学报》2021年第23期127-135,共9页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金(31961143019)。

摘　　要：针对当前育种群体小麦条锈病表型分析手段单一、效率低下等问题,该研究提出了一种基于无人机低空遥感和多光谱成像技术的群体小麦田间条锈病高通量表型动态分析方法。该方法利用无人机采集自然发病的育种群体小麦(共600个样本,516个基因型)冠层多时相的光谱图像,并提取22个植被指数作为后续分析的表型,同时按照发病后的时间顺序与传统条锈病人工鉴定标准记录条锈病发病阶段和发病严重度数据;使用随机森林算法建立22个光谱植被指数同条锈病发病阶段与病害严重度的分类模型,并筛选出对上述两个分类问题敏感的植被指数;同时,使用随机蛙跳算法对特征进行筛选以降低仅使用随机森林算法对特征筛选的偶然性,并将随机蛙跳算法给出被选择概率排名在约前1/3的特征作为SVM算法的输入,构建发病阶段与病害严重度模型以验证随机蛙跳算法对特征筛选的有效性;综合两次特征选择的结果,分别筛选出对发病阶段和病害严重度敏感的3个植被指数,并基于这些指数响应的时间序列分析了群体中6个参考品种发病动态的差异。对条锈病发病阶段的分类模型构建中,随机森林和SVM模型测试集的F1分数分别为0.970和0.985;对条锈病严重度等级分类中,二者的F1分数为0.741和0.780,表明通过所建立的模型可以实现对群体小麦发病阶段和病害严重度等级的分类,且随机森林算法和随机蛙跳算法都能够筛选出对条锈病发病阶段和病害严重度敏感的特征。筛选出的差分植被红边指数(Difference Vegetation Index-Rededge,DVIRE)的响应对病害胁迫较为敏感,可用于同时描述田间条锈病发病阶段和严重度。该研究提出的高通量表型分析方法,基于无人机成像光谱提取的植被指数对群体小麦田间条锈病进行时间序列动态分析,能够精准量化群体小麦受条锈病胁迫状态,并可为其他作物抗�Breeding for wheat stripe rust-resistance could effectively reduce the harm caused by stripe rust.Accurate and efficient phenotyping is an essential step in crop breeding.Furthermore,UAV multispectral imaging had been used for crops growth monitoring,pathological phenotyping,biochemical index estimation and yield prediction widely for it could overcome the limitation of traditional phenotyping methods which has low efficiency and accuracy.In order to solve the problems of poor means and low efficiency of phenotyping of stripe rust in wheat,a dynamic high-throughput phenotyping framework for field stripe rust in population wheat using the UAV multispectral imaging was proposed.The experiment was carried out in Caoxinzhuang experimental farm,Yangling,Shaanxi Province,China.The breeding wheat population with 516 genotypes was used as experimental materials and planted into 600 plots according to the augmented design.Using UAV low altitude remote sensing and multispectral imaging,canopy multispectral images of experimental population wheat infected with stripe rust naturally were collected.At the same time,the data of disease stage and disease severity of stripe rust were recorded according to the disease time and the traditional manual identification standard of stripe rust.After image mosaic,radiometric correction,geometric correction,segmentation of region of interest and background,image clipping and other operations are carried out on the UAV remote sensing raw data,22 vegetation indices were calculated to analyze.Using 22 spectral vegetation indices as classification features,we firstly established classification models of disease stage and disease severity in stripe rust,and screen out the vegetation indices sensitive to stripe rust disease stage and disease severity through random forest algorithm.At the same time,the random frog algorithm is also used to filter the original features to reduce the contingency caused by using the random forest algorithm to realize the feature screening merely.Using vegetation

关 键 词：无人机 遥感 机器学习 小麦条锈病 多光谱成像 高通量表型 动态分析 

分 类 号：S127[农业科学—农业基础科学]