叶绿素荧光成像技术的葡萄霜霉病早期检测  

Early Detection of Plasmopara Viticola Infection in Grapevine Leaves Using Chlorophyll Fluorescence Imaging

作　　者：张昭[1,2,3,4] 姚志凤 王鹏[1,3,4] 苏宝峰 刘斌 宋怀波[1,3,4] 何东健 徐炎 胡静波[2] ZHANG Zhao;YAO Zhi-feng;WANG Peng;SU Bao-feng;LIU Bin;SONG Huai-bo;HE Dong-jian;XU Yan;HU Jing-bo(College of Mechanical and Electronic Engineering,Northwest A&F University,Yangling 712100,China;College of Electronic and Electrical Engineering,Baoji University of Arts and Sciences,Baoji 721016,China;Key Laboratory of Agricultural Internet of Things,Ministry of Agriculture and Rural Affairs,Yangling 712100,China;Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service,Yangling 712100,China;College of Information Engineering,Northwest A&F University,Yangling 712100,China;College of Horticulture,Northwest A&F University,Yangling 712100,China;State Key Laboratory of Crop Stress Biology in Arid Areas,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,陕西杨凌712100 [2]宝鸡文理学院电子电气工程学院,陕西宝鸡721016 [3]农业农村部农业物联网重点实验室,陕西杨凌712100 [4]陕西省农业信息感知与智能服务重点实验室,陕西杨凌712100 [5]西北农林科技大学信息工程学院,陕西杨凌712100 [6]西北农林科技大学园艺学院,陕西杨凌712100 [7]旱区作物逆境生物学国家重点实验室,陕西杨凌712100

出　　处：《光谱学与光谱分析》2022年第4期1028-1035,共8页Spectroscopy and Spectral Analysis

基　　金：国家自然科学基金项目(31672115);国家重点研发计划项目(2018YDF1000307);宁夏回族自治区重点研发计划项目(2019BBF02013);陕西省重点研发计划项目(2021NY-041);广西重点研发计划项目课题(桂科21076001)资助。

摘　　要：葡萄霜霉病对葡萄生产构成严重威胁,尽早防治是治理霜霉病的关键。为了对该病进行早期检测,以PCR检测获取的霜霉病相对生物量作为霜霉病侵染的依据,从暗适应—光适应—暗弛豫3个光合生理状态连续变化过程中,采集80个人工接种霜霉菌叶片和80个健康对照叶片连续6 d的叶绿素荧光图像。对比健康和接种叶片叶绿素荧光动力学曲线、参数图像和参数值的差异,使用单因素方差分析评估叶绿素荧光参数对霜霉病侵染的敏感性,筛选叶绿素荧光参数最优特征子集,使用机器学习分类器构建霜霉病早期检测模型。结果表明,随着接种后天数(day post inoculation,DPI)的增加,霜霉病侵染程度不断加深,健康和接种叶片叶绿素荧光动力学曲线、参数图像和参数值从2DPI开始有显著差异(p<0.05),霜霉病侵染导致叶片光化学猝灭速率减小(Rfd变小),光合效率降低(F_(v)/F_(m)变小),叶片活力和光保护能力衰退(NPQ和qN变小),叶片吸收的光能更多以荧光的形式释放出来(F_(t)和F_(m)变大)。基于序列前向浮动算法优选的叶绿素荧光参数特征子集(qN-L3,Rfd-L2,NPQ-L1和F_(v)/F_(m)-D1)和BP神经网络分类器的SFFS-BP模型对3DPI健康和接种叶片识别准确率为83.75%,全实验周期连续6 d平均准确率达到85.94%。可为葡萄霜霉病光合表型分析和早期检测提供一种快速、准确的手段。Plasmopara viticola(P.viticola)infection poses a serious threat to grape production.Early prevention and treatment is essential to the control of P.viticola infection.In order to detect this disease early,the relative biomass of P.viticola detected by PCR as the basis of P.viticola infection,the chlorophyll fluorescence images of 80 grape leaves inoculated with P.viticola and 80 healthy control leaves were collected for 6 consecutive days from the three continuous changes of photosynthetic physiological state,namely dark adaptation,light adaptation and dark relaxation,using the relative biomass of downy fungus as the basis of P.viticola infection.The sensitivity of chlorophyll fluorescence parameters to downy mildew infection was evaluated by one-way analysis of variance(ANOVA).The optimal feature subset of chlorophyll fluorescence parameters extracted by feature selection strategies was input to machine learning classifiers to establish the early detection model of P.viticola infection.The results showed that with the increase of DPI,the degree of downy mildew infection was deepened,and the chlorophyll fluorescence dynamics curves and parameters of healthy and inoculated leaves were significantly different from 2 DPI(p<0.01).Due to the infection,the photochemical quenching rate of inoculated leaves decreased(Rfd decreased),and the photosynthetic efficiency decreased(F_(v)/F_(m) decreased).Leaf vitality and photoprotection ability continued to decline(NPQ and qN decreased),and the light energy absorbed by leaves was more released in the form of fluorescence(F_(t) and F_(m) increased).BP neural network model using the feature subset(qN-L3,RFD-L2,NPQ-L1 and F_(v)/F_(m)-D1) optimized by the SFFS algorithm had the best detection accuracy,and the detection accuracy of healthy,and inoculated leaves at 3 DPI was 83.75%.The average accuracy of the whole experiment period for 6 consecutive days reached 85.94%.These results provide a fast and accurate method for photosynthetic phenotype analysis and early detection of grap

关 键 词：叶绿素荧光成像 葡萄霜霉病 病害检测 特征选择 

分 类 号：O433[机械工程—光学工程]