【作者】 曹学仁；

【导师】 周益林；

【作者基本信息】 中国农业科学院 ， 植物病理学， 2012， 博士

【摘要】 白粉病是我国小麦生产上重要的病害之一。本研究于2010年和2011年利用地面高光谱遥感和无人机数字图像对田间2种植密度下小麦白粉病的发生情况进行了研究，分析了其在白粉病为害后小麦产量、千粒重和籽粒蛋白质损失估计研究中的可行性，利用孢子捕捉器技术研究了空气中白粉菌分生孢子浓度的变化动态与病情的关系，开发了基于Real-time PCR技术的捕捉器中白粉菌孢子浓度的定量检测技术，这些研究结果为遥感、孢子捕捉和分子生物学等技术在白粉病监测预警中的应用奠定了科学基础。本文取得的主要结论如下：白粉病发生后在扬花期、灌浆初期和灌浆后期对2种植密度下田间小麦冠层近红外波段的光谱反射率产生了显著影响。红边斜率（dλred）、红边面积（SDr）及植被指数DVI和SAVI在2年度2种植密度下和病情指数存在显著地相关性。建立了扬花期、灌浆初期和灌浆后期基于冠层光谱参数的田间小麦白粉病病情的估计模型，所建模型的斜率在2种植密度下无显著差异，说明种植密度不影响冠层光谱参数对小麦白粉病发生程度的估计。在正常的小麦种植密度条件下，距地面200m、300m和400m无人机数字图像的颜色特征参数在2年度与田间小麦白粉病病情存在显著或极显著地相关性；在种植密度较小的条件下，距地面不同高度的无人机数字图像中仅G（绿）和I（亮度）在2年度与病情存在显著地相关性。2年度基于I所建的病情估计模型在正常种植密度下无显著差异。白粉病发生后对高感品种京双16产量、千粒重及籽粒蛋白质含量造成的损失分别可高达40%、16%和10%左右。可以选择扬花期SDr和dλred来估计白粉病发生后田间小麦的产量，灌浆后期的DVI和SDr可估计病害发生后小麦的千粒重和蛋白质含量。距地面不同高度数字图像的颜色特征参数与产量、千粒重和蛋白质含量存在显著地相关性。空气中白粉菌的分生孢子浓度首先逐渐增加，在灌浆期达到最大值后逐渐减小。空气中的分生孢子浓度和温度、太阳辐射及风速有关。时间序列分析结果表明，2年度空气中白粉菌分生孢子浓度均符合ARIMA（1,1,0）模型。分别建立了基于当期和7天前空气中孢子浓度、气象因子以及空气中孢子浓度和气象因子的病情指数的关系模型，其中以基于空气中孢子浓度所建模型更适合在生产上推广应用。在对捕捉带上孢子洗脱和破壁后，和试剂盒法相比，传统CTAB法的提取效率更高。Real-timePCR技术获得的孢子浓度和显微镜下计数获得的孢子浓度具有极显著地正相关性，且Real-timePCR技术具有特异性、准确性和工作效率高等优点，表明Real-time PCR技术可用于对捕捉带上病菌分子孢子的定量检测。更多还原

【Abstract】 Wheat powdery mildew, caused by Blumeria graminis f. sp. triciti, is a destructive foliar disease inChina. In this study, hyperspectral canopy reflectance spectra and digital images acquired fromunmanned air vehicle (UAV) was used to detect wheat powdery mildew at two plant density levels in2010and2011. Also the using of canopy reflectance and digital images in wheat yield,1000-kernelweight and protein content estimation was studied. Dynamics in concentrations of Bgt conidia and itsrelationship to local weather conditions and disease index in wheat was analyzed, and disease predictionmodels based on field data was constructed. Airborne spores of Bgt quantification method usingreal-time PCR were developed. These results provide scientific foundation for the application of remotesensing, spore trap and molecular biology technique in Monitoring and Early Warning in wheatpowdery mildew. The main results were as follows:The canopy reflectance of wheat at two plant density levels at anthesis, early milk and late milkstage was influenced by powdery mildew in2010and2011, especially at NIR region. Red edge slope(dλred), the area of the red edge peak (SDr), DVI and SAVI have significant correlations with diseaseindexes at two plant density levels in both years. Disease detection models were constructed based onspectral parameters for both density levels at anthesis, early milk and late milk stage in two years.Moreover, there was no significant difference in slope of the constructed models between two densitylevels in both years. This indicated that plant densities didn’t affect the use of spectral parameters inwheat powdery mildew estimation.At normal plant density level, color features and their combinations extracted from digital imagesfrom200m,300m and400m above the ground have significant correlations with disease indexes inboth years. While at low plant density level, only G (green) and I (intensity) have significantcorrelations with disease indexes in both years. There was no significant difference between diseaseindexes estimation models based on I in both years at normal plant density level.Loss of yield,1000-kernel weight and protein content of wheat could reach as high as40%,16%and10%for Jingshuang16(susceptible cultivar) when disease occurred. dλredand the area of the SDr atanthesis stage could be used to estimation wheat yield when powdery mildew occurred. DVI and SDr atlate milk stage could be used to estimation1000-kernel weight and protein content after powderymildew occurred. Color features extracted from digital images200m,300m and400m above theground were significantly correlated with yield,1000-kernel weight and protein content.Conidia increased gradually with time. The highest conidial concentrations in the air wereobserved at milk stage. The concentrations of Bgt conidia in the air were correlated with temperature,solar radiation and wind speed. Time series analysis, using autoregressive integrated moving average(ARIMA)(p, d, q) models, showed that each of the three season’s data can be fitted with ARIMA (1,1,0) models. Three sets of models were derived by inoculum only, by weather variables only, and by both inoculum and weather variables to the disease index. And there was no significant difference betweenmodels using inoculum only in2010and2011. Model derived by inoculum are more suitable inproduction practice.After spores were eluted and disrupted from tapes, the extraction efficiency of DNA usingtraditional CTAB method was higher compared with kit method. A significant linear relationshipbetween conidial concentrations counted with a compound microscope and those determined with thereal-time PCR assay was obtained, using the same samples of spore traps. Real-time PCR was specific,accurate and more efficiency when compared with compound microscope. The results demonstrated apotential method to quantitatively determine spore inoculum potential in traps by using real-time PCR.更多还原