基于不同叶位日光诱导叶绿素荧光信息的水稻叶瘟病早期监测  

Spectroscopic Detection of Rice Leaf Blast Infection at Different Leaf Positions at The Early Stages With Solar Induced Chlorophyll Fluorescence

作　　者：程宇馨 薛博文 孔媛媛 姚东良 田龙 王雪[1] 姚霞[1] 朱艳[1] 曹卫星[1] 程涛[1] CHENG Yuxin;XUE Bowen;KONG Yuanyuan;YAO Dongliang;TIAN Long;WANG Xue;YAO Xia;ZHU Yan;CAO Weixing;CHENG Tao(National Engineering and Technology Center for Information Agriculture(NETCIA)/Jiangsu Key Laboratory for Infor‐mation Agriculture/MARA Key Laboratory of Crop System Analysis and Decision Making/MOE Engineering Research Center of Smart Agriculture,Nanjing Agricultural University,Nanjing 210095,China)

机构地区：[1]南京农业大学国家信息农业工程技术中心/江苏省信息农业重点实验室/农业农村部农作物系统分析与决策重点实验室/智慧农业教育部工程研究中心/现代作物生产省部共建协同创新中心,江苏南京210095

出　　处：《智慧农业（中英文）》2023年第3期35-48,共14页Smart Agriculture

基　　金：江苏省大学生创新创业训练计划(202110307101Y);国家自然科学基金面上项目(41871259)。

摘　　要：[目的/意义]基于遥感手段的稻叶瘟(Rice Leaf Blast,RLB)无损早期监测对于抗性育种和植保防控具有重要作用。目前对稻瘟病的研究多使用反射光谱在其显症阶段进行监测,针对稻叶瘟早期侵染阶段的日光诱导叶绿素荧光(Solar-Induced Chlorophyll Fluorescence,SIF)光谱监测研究尚未见报道。本研究的目的是基于不同叶位的日光诱导叶绿素荧光信息,实现水稻叶瘟病早期阶段感病叶片的准确识别。[方法]基于一年的温室接种试验和大田采样实验,配合使用主动光源、ASD(Analytical Spectral Devices)地物光谱仪和FluoWat叶片夹,获取了拔节期和抽穗期水稻植株顶1至顶4叶位的叶片SIF光谱,并人工标注了被测样本的发病等级。研究基于连续小波分析(Continue Wavelet Analysis,CWA)提取对稻叶瘟敏感的小波特征,比较了不同叶位敏感特征及其感病叶片识别精度,最后基于线性判别分析(Linear Discriminant Analysis,LDA)算法构建了稻叶瘟识别模型。[结果和讨论]各叶位感病叶片远红光区域的上行和下行SIF均显著高于健康叶片;基于SIF小波特征的感病叶片识别精度显著高于原始SIF波段,顶1叶的稻瘟病识别精度显著高于其他三个叶位,其识别精度最高可达70%;提取的适用于多叶位的共性敏感小波特征↑WF832,3和↓WF809,3在顶1至顶4叶的精度分别达到69.45%、62.19%、60.35%、63.00%和69.98%、62.78%、60.51%、61.30%。[结论]本研究揭示了稻瘟病胁迫下水稻叶片SIF光谱响应规律,提取了对稻叶瘟敏感的SIF小波特征,结果证明了连续小波分析和SIF技术用于诊断稻叶瘟的潜力,为实现稻瘟病的田间早期、快速、原位诊断提供了重要参考与技术支撑。[Objective]Rice blast is considered as the most destructive disease that threatens global rice production and causes severe economic losses worldwide.The detection of rice blast in an early manner plays an important role in resistance breeding and plant protection.At present,most studies on rice blast detection have been devoted to its symptomatic stage,while none of previous studies have used so‐lar-induced chlorophyll fluorescence(SIF)to monitor rice leaf blast(RLB)at early stages.This research was conducted to investigate the early identification of RLB infected leaves based on solar-induced chlorophyll fluorescence at different leaf positions.[Methods]Greenhouse experiments and field trials were conducted separately in Nanjing and Nantong in July and August,2021,in order to record SIF data of the top 1th to 4th leaves of rice plants at jointing and heading stages with an Analytical Spectral Devices(ASD)spectrometer coupled with a FluoWat leaf clip and a halogen lamp.At the same time,the disease severity levels of the mea‐sured samples were manually collected according to the GB/T 15790-2009 standard.After the continuous wavelet transform(CWT)of SIF spectra,separability assessment and feature selection were applied to SIF spectra.Wavelet features sensitive to RLB were extract‐ed,and the sensitive features and their identification accuracy of infected leaves for different leaf positions were compared.Finally,RLB identification models were constructed based on linear discriminant analysis(LDA).[Results and Discussion]The results showed that the upward and downward SIF in the far-red region of infected leaves at each leaf position were significantly higher than those of healthy leaves.This may be due to the infection of the fungal pathogen Magnaporthe oryzae,which may have destroyed the chloroplast structure,and ultimately inhibited the primary reaction of photosynthesis.In addi‐tion,both the upward and downward SIF in the red region and the far-red region increased with the decrease of leaf position.The

关 键 词：稻瘟病 日光诱导叶绿素荧光 连续小波光谱分析 叶位 早期病害监测 

分 类 号：S511[农业科学—作物学] S363