植物病害監(jiān)測預警新技術(shù)研究進展

曹學仁,　周益林

(1. 中國農(nóng)業(yè)科學院植物保護研究所,植物病蟲害生物學國家重點實驗室, 北京　100193; 2. 中國熱帶農(nóng)業(yè)科學院環(huán)境與植物保護研究所,農(nóng)業(yè)部熱帶作物有害生物綜合治理重點實驗室, 海口　571101)

?

植物病害監(jiān)測預警新技術(shù)研究進展

曹學仁1,2,周益林1*

(1. 中國農(nóng)業(yè)科學院植物保護研究所,植物病蟲害生物學國家重點實驗室, 北京100193; 2. 中國熱帶農(nóng)業(yè)科學院環(huán)境與植物保護研究所,農(nóng)業(yè)部熱帶作物有害生物綜合治理重點實驗室, ?？?71101)

植物病害監(jiān)測預警對病害防治和管理具有重要的意義,本文綜述了“3S”技術(shù)、孢子捕捉技術(shù)、軌跡分析技術(shù)、分子生物學技術(shù)等在植物病害監(jiān)測預警研究中的應用,同時探討了本研究領(lǐng)域的發(fā)展方向。

植物病害;監(jiān)測預警;“3S”技術(shù);孢子捕捉;分子生物學

植物病害監(jiān)測預警是制定病害防治措施的前提和基礎(chǔ),傳統(tǒng)的植物病害監(jiān)測方法主要依靠田間人工調(diào)查獲取數(shù)據(jù),預測預報多采用數(shù)理統(tǒng)計、綜合分析方法等,近些年來“3S”技術(shù)(遙感技術(shù)、地理信息系統(tǒng)、全球定位系統(tǒng))、分子生物學技術(shù)以及一些相關(guān)學科如空氣生物學、生物氣象學的快速發(fā)展,極大地促進了植物病害監(jiān)測預警技術(shù)的研究發(fā)展。

1　“3S”技術(shù)在植物病害監(jiān)測預警中的應用研究

1.1遙感技術(shù)在植物病害監(jiān)測預警中的應用

遙感技術(shù)(RS)通過處理和解釋接受目標物輻射或反射的電磁波,能夠準確而快速地提供被測目標物的相關(guān)信息[1]。由于遙感技術(shù)能感受到人類肉眼看不到的光,可利用的電磁波波長為0.3 μm～3 m,同時這種技術(shù)還具有監(jiān)測面積大,獲得資料快速、規(guī)范,數(shù)據(jù)能直接輸入電腦等優(yōu)點,已廣泛應用于軍事、氣象、地質(zhì)、農(nóng)業(yè)等領(lǐng)域。植物病害的遙感監(jiān)測開始于20世紀30年代早期,將近紅外航空圖像應用于馬鈴薯和煙草病毒病的監(jiān)測[2]。當植物受到病害危害后,葉片會出現(xiàn)顏色改變、結(jié)構(gòu)破壞或外形變化等癥狀,其反射光譜曲線會發(fā)生明顯改變。一般在藍光和紅光波段,發(fā)病植物的反射率比健康植物的大,而近紅外波段,發(fā)病植物的反射率比健康植物的小[3]。據(jù)此可以利用遙感技術(shù)進行病害診斷和監(jiān)測等研究,即當病害發(fā)生后,從遙感圖像上提取植被的相關(guān)信息,快速、準確地判斷出病害發(fā)生的位置、面積和程度,從而采取針對性的點、片防治措施。

根據(jù)平臺可將遙感分為近地遙感、航空遙感和衛(wèi)星遙感。近地遙感主要是通過利用光譜儀在實驗室及田間測量農(nóng)作物葉片及冠層受病害危害后的光譜反射率,它具有操作簡單、信息量大、數(shù)據(jù)易處理分析等優(yōu)點,是目前植物病害遙感監(jiān)測中研究最多的。國內(nèi)外已有關(guān)于利用近地遙感監(jiān)測玉米矮花葉病和小斑病[4]、馬鈴薯晚疫病[5]、甜菜褐斑病、白粉病和銹病[6]、稻瘟病[78]、小麥條銹病[913]、甜菜叢根病[14]、小麥黃斑葉枯病[15]、小麥葉枯病[16]、小麥全蝕病[17]、芹菜菌核病[18]、小麥赤霉病[19]、棉花黃萎病[2021]等在內(nèi)的多種植物病害的研究報道。Cao等利用高光譜儀對2個抗感性不同的品種、2種不同種植密度下受白粉病危害后的小麥冠層光譜反射率進行了研究,獲得了可用于小麥白粉病監(jiān)測的敏感光譜參數(shù)和時期,建立了病害監(jiān)測模型,發(fā)現(xiàn)品種和密度對利用高光譜遙感監(jiān)測小麥白粉病無顯著影響[2223]。但是地面遙感獲取的面積比較小,與農(nóng)作物大面積種植相比,其應用還受到一定的限制。航空遙感一般以無人機、氣球等航空飛行器為平臺,與地面高光譜遙感相比,雖然信息量減少,但一次可監(jiān)測的面積大、數(shù)據(jù)獲取快捷。目前已有其在栗樹疫病[24]、馬鈴薯晚疫病[25]、小麥葉枯病[26]、水稻白葉枯病[27]、柑橘黃龍病[28]、小麥條銹病[13]、月桂枯萎病[29]等病害上的研究報道。隨著衛(wèi)星數(shù)量的增多和分辨率的提高,衛(wèi)星遙感也開始應用于植物病害監(jiān)測,包括小麥葉銹病[30]、小麥條銹病[31]、小麥線條花葉病[32]、小麥白粉病[33]、柑橘黃龍病[34]等多種植物病害。已有的研究還發(fā)現(xiàn)利用遙感技術(shù)還可將目標病害與其他病蟲害和生理性病害區(qū)分開來。Mahlein等[6]研究發(fā)現(xiàn)利用光譜植被指數(shù)可以區(qū)分甜菜褐斑病、銹病和白粉病。采用Fisher線性判別分析(FLDA)和偏最小二乘回歸法(PLSR)結(jié)合光譜反射率可以將小麥條銹病、白粉病和蚜蟲區(qū)分開來[35]。因此,利用遙感技術(shù)可以用來區(qū)分和監(jiān)測植物病害。

1.2地理信息系統(tǒng)在植物病害監(jiān)測預警中的應用

地理信息系統(tǒng)(GIS)是一個用于輸入、存儲、檢索、分析和顯示空間地理數(shù)據(jù)的計算機軟件平臺。將地面調(diào)查獲取的植物病害的相關(guān)信息保存在GIS的數(shù)據(jù)庫中,通過數(shù)據(jù)處理對同一區(qū)域或相鄰的區(qū)域病害的空間分布和發(fā)生程度進行監(jiān)測。司麗麗等成功地研制出了基于地理信息系統(tǒng)的全國主要糧食作物病蟲害實時監(jiān)測預警系統(tǒng),利用該系統(tǒng)能夠?qū)π←湣⒂衩?、水稻、馬鈴薯、高粱和谷子6種主要糧食作物的60余種病蟲害進行實時監(jiān)測和預警[36]。同時GIS也能和病害預測模型結(jié)合,實現(xiàn)對病害發(fā)生情況的預測。Hijmans等應用地理信息系統(tǒng)結(jié)合馬鈴薯晚疫病發(fā)生的兩個預測模型Blitecast和Simcast,對全球馬鈴薯晚疫病的發(fā)生情況進行了預測,發(fā)現(xiàn)晚疫病的高發(fā)區(qū)主要包括歐洲西部、美國北部、加拿大東部沿海、巴西東南部和中國中南部地區(qū),而病害低發(fā)區(qū)主要在印度西部平原、中國的中北部地區(qū)、美國中西部地區(qū)[37]。馬占鴻等和李伯寧等利用GIS技術(shù)分別對小麥條銹病和白粉病在我國的越夏區(qū)進行了區(qū)劃研究,明確了小麥條銹病和白粉病在我國的越夏范圍[3839]。

1.3“3S”技術(shù)一體化在植物病害監(jiān)測預警中的應用

3S技術(shù)是將遙感、地理信息系統(tǒng)和全球定位系統(tǒng)三門學科有機結(jié)合,構(gòu)成的一個集信息獲取、處理和應用一體化的技術(shù)系統(tǒng)。其監(jiān)測植物病害的基本流程是:RS提供的圖像將作為植物病害監(jiān)測的數(shù)據(jù)源,通過軟件對RS圖像進行分析,得到病害發(fā)生區(qū)及程度;利用GIS對圖像進一步分析,確定病情發(fā)生點的精確地理坐標和面積等所需信息;全球定位系統(tǒng)(GPS)作為定位空間地理位置精確坐標的工具,幫助找到病害不同發(fā)生點的準確位置。美國Iowa州立大學Nutter等運用地面GPS定位,通過地面高光譜測量、小型飛機搭載光譜儀低空飛行和Landsat-7分別獲得地面、航空和衛(wèi)星三個不同平臺的遙感數(shù)據(jù),利用GIS系統(tǒng)進行數(shù)據(jù)分析,監(jiān)測大豆孢囊線蟲(Heteroderaglycines)的危害范圍和危害程度,建立了田間病情與地面光譜以及航空和衛(wèi)星遙感數(shù)據(jù)的關(guān)系[40]?！?S”技術(shù)使植保研究的病害信息及環(huán)境信息的獲取、采集、分析利用更加自動化、科學化,提高對農(nóng)業(yè)有害生物的監(jiān)測預警能力和綜合治理水平,是未來監(jiān)測作物病害的發(fā)展趨勢。

2　基于病原菌孢子捕捉器技術(shù)的病害監(jiān)測預警研究

對于氣傳性多循環(huán)真菌病害(如白粉病、銹病等)來說,病原菌孢子隨氣流傳播是病害發(fā)生和流行的主要原因,因此空氣中病原菌孢子的數(shù)量和病害的發(fā)生有密切的關(guān)系,通過對空氣中孢子捕捉可以為病害預測預報提供基礎(chǔ)數(shù)據(jù)。用于空氣中孢子捕捉的方法主要包括水平玻片法[41]、垂直或傾斜玻片法或垂直圓柱體法[42]、定容式孢子捕捉器法[43]以及移動式孢子捕捉器法[44]。但是前兩種方法的孢子捕捉效率受到氣候(如降雨、風速)和捕捉表面達到過飽和的影響,而移動式孢子捕捉器主要用于取樣,不能對病原菌數(shù)量進行連續(xù)監(jiān)測。因此在對空氣中病原菌的動態(tài)監(jiān)測上,應用最多的是定容式孢子捕捉器。

這種類型的孢子捕捉器的原理是利用空氣驅(qū)動裝置使捕捉倉內(nèi)形成負壓,空氣經(jīng)進氣嘴就被吸入到捕捉倉內(nèi),從而空氣中的孢子就被吸附到捕捉盤上的黏性捕捉帶上。通過安裝定時鐘,捕捉盤能按一定的速度轉(zhuǎn)動,不同時段空氣中的孢子數(shù)據(jù)就記錄在捕捉帶的不同位置,不僅避免了捕捉表面達到過飽和,而且也可實現(xiàn)對病原菌數(shù)量的連續(xù)監(jiān)測。由于進氣嘴的大小和進氣速度都可以確定,因此可以計算出單位時間內(nèi)每立方米空氣中病原菌孢子的數(shù)量。

利用孢子捕捉器獲得的孢子數(shù)或濃度數(shù)據(jù),結(jié)合氣象數(shù)據(jù)和病情調(diào)查數(shù)據(jù),就可以分析三者之間的關(guān)系,最后建立病害預測模型。Cao等對空氣中小麥白粉病菌(Blumeriagraminisf.sp.tritici)分生孢子濃度的季節(jié)性和日變化動態(tài)進行了監(jiān)測,分析了分生孢子濃度和氣象因子、病情之間的關(guān)系,最后分別建立了基于氣象因子、孢子濃度或氣象因子和孢子濃度的小麥白粉病病害預測模型[4546]?；诳諝庵刑O果白粉病菌(Podosphaeraleucotricha)[47]、草莓灰霉病菌(Botrytiscinerea)[4849]、葡萄白粉病菌(Erysiphenecator)[50]、草莓白粉病菌(Sphaerothecamacularis)[51]、甜菜褐斑病菌(Cercosporabeticola)等病原菌孢子濃度或孢子數(shù)[52]的病害預測模型也已報道。

3　基于軌跡分析技術(shù)的植物病害監(jiān)測預警研究

對于遠距離傳播的氣傳性病原菌(如小麥條銹病菌和稈銹病菌、大豆銹病菌等)來說,研究病原菌隨氣流的傳播路線、距離和菌源區(qū)和著落區(qū)之間菌量的關(guān)系及發(fā)生時間,將為病害監(jiān)測預警提供新的方法。植物病原菌隨氣流的遠距離傳播是一個被動的過程,需要病原菌傳播體(孢子)被氣流抬送到一定的高度,才能在高空隨大氣環(huán)流進行遠距離傳播,因此氣流是植物病原菌遠距離傳播的主要動力。相關(guān)氣流運動的物理模型已成為研究病原菌遠距離傳播的有力工具,軌跡分析是氣流運動的物理模型中最常見的一種方法,其中在植物病原菌遠距離傳播中的研究報道僅見大氣質(zhì)點軌跡分析平臺Hysplit(Hybird Single-Particle Lagrangian Integrated Trajectory)在大豆銹病菌(Phakopsorapachyrhizi)、小麥條銹病菌(Pucciniastriiformisf.sp.tritici)和稈銹病菌(P.graminisf.sp.tritici)新毒性小種Ug99遠距離傳播研究中的運用。

Pan等[53]利用Hysplit_4結(jié)合區(qū)域氣候預測模型(MM5)對大豆銹病菌孢子在洲際間的遠距離傳播進行了研究,根據(jù)孢子量的多少和分布來估計大豆銹病的病情和傳播,結(jié)果表明該方法不僅可以用來模擬病原菌的傳播路線和分布,還可以用來指導大豆銹病的早期預警和監(jiān)測。

Wang等[54]、王海光等[55]利用Hysplit_4研究了小麥條銹病菌在我國的遠距離傳播規(guī)律,分析了西北、華北、西南之間的菌源關(guān)系。

國際玉米小麥改良中心(CIMMYT)研究人員也采用Hysplit對1999年在烏干達首次發(fā)現(xiàn)的強毒性小麥稈銹病菌小種Ug99的遠距離傳播進行了分析和預測,分析以2007年Ug99已傳入的伊朗為菌源地,結(jié)果發(fā)現(xiàn),病菌不但可能隨氣流向東傳播,也有可能向北傳播到高加索和中亞地區(qū)[56]。

4　基于分子生物學技術(shù)的植物病害監(jiān)測預警研究

目前,分子生物學技術(shù)已經(jīng)滲透到幾乎所有的生物學領(lǐng)域,成為21世紀應用于農(nóng)業(yè)的兩大高新技術(shù)之一。近年來,分子生物學技術(shù)在植物病原菌監(jiān)測上也開始得到了應用。

4.1在菌源量檢測和監(jiān)測上的應用

病害一般在發(fā)生初期或越冬越夏階段往往處于潛伏狀態(tài),而此階段病害菌源量的準確估計對病害流行預測預報十分重要,它是預測病害發(fā)展趨勢的重要參數(shù)。但使用常規(guī)方法調(diào)查病害時,用肉眼無法觀測到處于潛育狀態(tài)的植物病害,而葉片培養(yǎng)法費工費時,且受環(huán)境干擾大,結(jié)果誤差也比較大。快速發(fā)展的分子生物學方法和技術(shù)為此提供了強有力的工具,它可解決一些用傳統(tǒng)植病流行學方法無法或很難解決的問題。如利用Nested-PCR技術(shù),檢測到了潛伏侵染的稻曲病菌(Ustilaginoideavirens)[57]、油菜葉斑病菌(Pyrenopezizabrassicae)[58]、葡萄座腔菌(Botryosphaeriadothidea)[59]和小麥白粉病菌[59]等。真正實現(xiàn)對病原菌的定量檢測,要得益于近年來Real-time PCR在這方面的應用,Real-time PCR可對植物葉片中病原菌侵染程度進行定量分析。閆佳慧等和鄭亞明等利用Real-time PCR分別對田間不同地區(qū)未顯癥小麥葉片進行檢測,并與實際調(diào)查小麥條銹病和白粉病病情指數(shù)或取樣培養(yǎng)發(fā)病的病情指數(shù)進行比較,結(jié)果表明不同地區(qū)小麥葉片樣品Real-time PCR檢測的MDX與實際病情指數(shù)DX之間有顯著的相關(guān)性[6162]。

在對空氣中病原菌進行取樣監(jiān)測時(如用孢子捕捉器),常規(guī)的病菌孢子種類鑒定和計數(shù)方法是在顯微鏡下根據(jù)孢子的形態(tài)特征來判斷,該方法需要的時間長、工作量大,且有些病原菌孢子的形態(tài)特征相似容易產(chǎn)生誤判。分子生物學技術(shù)在對空氣中病原菌的檢測上也得到了應用。Williams等首先報道了孢子捕捉器捕捉帶上孢子DNA的提取方法[63]。Calderon等成功地提取了Burkard孢子捕捉器捕捉到的2種油菜重要病原菌Leptosphaeriamaculans和Pyrenopezizabrassicae的DNA,發(fā)現(xiàn)PCR技術(shù)可檢測的最低孢子數(shù)分別為1個和10個左右[64]。此外空氣中油菜菌核病菌(Sclerotiniasclerotiorum)[65]、葡萄白粉病菌(Erysiphenecator)[66]和啤酒花霜霉病菌(Pseudoperonosporahumuli)[67]等的分子檢測技術(shù)也已報道。

Real-time PCR不僅可對孢子捕捉器樣本中的孢子進行鑒定,更重要的是可以進行定量分析。該技術(shù)近年來也開始應用于空氣中病原菌濃度定量研究。Fraaije 等利用孢子捕捉器和Real-time PCR技術(shù),研究了小麥殼針孢葉枯病菌(Mycosphaerellagraminicola)子囊孢子在病原菌對QoI類殺菌劑抗性傳播中的作用[68]。Luo等利用Real-time PCR技術(shù)測定Burkard孢子捕捉器樣品中核果褐腐病菌(Moniliniafructicola)孢子的DNA濃度,定量估計空氣中此病原菌的孢子濃度,它與顯微鏡孢子計數(shù)方法的結(jié)果一致[69]。曹學仁等也成功開發(fā)出用于定量檢測Burkard孢子捕捉器樣品中小麥白粉病菌孢子量的Real-time PCR技術(shù)[70]。孢子捕捉器上的油菜黑脛病菌(Leptosphaeriamaculans和L.biglobosa)[71]、油菜菌核病菌(S.sclerotiorum)[72]、蔥鱗葡萄孢菌(Botrytissquamosa)[73]、油菜葉斑病菌(P.brassicae)[74]、蘋果黑星病菌(Venturiainaequalis)[75]等病菌的實時定量PCR檢測技術(shù)都已有報道。

4.2在生理小種和抗藥性監(jiān)測上的應用

由于常規(guī)的生理小種鑒定及監(jiān)測均基于鑒別寄主,分析方法繁雜、費工費時,其結(jié)果易受鑒定條件、人員等外部條件的影響。利用分子生物學技術(shù)特別是分子標記可以較好地解決這一問題。如條銹菌條中29號、31號、32號和33號生理小種的SCAR檢測標記已建立[7678]。劉景梅等在香蕉枯萎病菌上也獲得了尖孢鐮刀菌古巴?；蚏ace 1和Race 4的SCAR標記[79]。利用這類?；瘶擞浛梢灾苯舆M行生理小種的分子鑒定和各生理小種的田間流行動態(tài)監(jiān)測,不僅準確性高,而且縮短了時間。

分子技術(shù)檢測方法特別是Real-time PCR檢測方法也開始在殺菌劑抗性監(jiān)測中應用,此方法不但高通量、快速,而且準確性也較高,尤其適于不能在人工培養(yǎng)基上培養(yǎng)的專性寄生菌。采用這種方法可對低頻率的殺菌劑抗性基因進行早期檢測,并結(jié)合抗藥性的風險評估,有利于進一步的抗性風險評估和制定有效的抗性策略。如李紅霞等基于油菜菌核病菌(S.sclerotiorum)抗藥性菌株β-微管蛋白基因的突變,開發(fā)出了用于檢測油菜菌核病菌對多菌靈抗藥性的PCR方法,其檢測所得結(jié)果與傳統(tǒng)菌落直徑法結(jié)果相吻合[80]。Fraaije等采用定量熒光等位基因特異性實時PCR方法,可檢測小麥白粉病菌抗甲氧基丙烯酸酯類殺菌劑發(fā)生位點突變的菌株,用此方法可快速監(jiān)測使用殺菌劑前后田間發(fā)生突變的小麥白粉病菌菌株的動態(tài)變化[81]。利用Real-time PCR技術(shù)監(jiān)測田間褐腐病菌(M.fructicola)對苯并咪唑類殺菌劑[82]、小麥白粉病菌對三唑酮[83]以及葡萄白粉病菌(E.necator)對DMIs殺菌劑及QoIs類殺菌劑[84]的抗性頻率已經(jīng)報道。

5　植物病害監(jiān)測預警技術(shù)研究展望

綜上所述,近年來隨著“3S”技術(shù)(GPS、GIS和RS)、電子傳感技術(shù)(電子鼻、電子舌等)[85]、分子生物學技術(shù)等相關(guān)學科的快速發(fā)展,大大促進了病害的監(jiān)測預警技術(shù)的發(fā)展,一些技術(shù)如GPS技術(shù)和GIS技術(shù)已普遍應用于病害調(diào)查和研究中,遙感技術(shù)也已顯現(xiàn)它廣闊的應用前景,而且隨著衛(wèi)星分辨率的提高和高分辨率衛(wèi)星如Quickbird、IKONOS、GeoEye、高分系列衛(wèi)星等應用的廣泛性,可實現(xiàn)對植物病害整體的、實時的和動態(tài)監(jiān)測和分析,特別是近年來實時定量PCR檢測技術(shù)的發(fā)展,為病害的早期和高通量監(jiān)測提供了強有力的工具,因此未來植物病害流行的監(jiān)測和預警,將具備微觀和宏觀的雙重手段,從而全面提高對病害的監(jiān)測預警準確性。盡管國內(nèi)的一些研究單位或?qū)嶒炇乙言谶@方面做了一些探索性的工作,但總體來說目前我國對重要植物病害的監(jiān)測和預警還比較薄弱,今后還應加強這方面的工作,使這些新技術(shù)盡快在生產(chǎn)上得到應用,以提高我國植物病害監(jiān)測和預警的水平。

[1]梅安新, 彭望琭, 秦其明, 等. 遙感導論[M]. 北京:高等教育出版社, 2001.

[2]Bawden F C. Infra-red photography and plant virus diseases[J]. Nature, 1933, 132:168.

[3]West J S, Bravo C, Oberti R, et al. The potential of optical canopy measurement for targeted control of field crop disease[J]. Annual Review of Phytopathology, 2003, 41:593614.

[4]Ausmus B S, Hilty J W. Reflectance studies of healthy, maize dwarf mosaic virus-infected, andHelminthosporiummaydis-infected corn leaves[J]. Remote Sensing of Environment, 1972, 2:7781.

[5]Zhang Minghua, Qin Zhihao, Liu Xue, et al. Detection of stress in tomatoes induced by late blight disease in California, USA, using hyperspectral remote sensing [J]. International Journal of Applied Earth Observation and Geoinformation, 2003, 4:295310.

[6]Mahlein A K, Steiner U, Dehne H W, et al. Spectral signatures of sugar beet leaves for the detection and differentiation of diseases [J]. Precision Agriculture, 2010, 11:413431.

[7]Kobayashi T, Kanda E, Kitada K, et al. Detection of rice panicle blast with multispectral radiometer and the potential of using airborne multispectral scanners[J]. Phytopathology, 2001, 91(3):316323.

[8]吳曙雯, 王人潮, 陳曉斌, 等. 稻葉瘟對水稻光譜特性的影響研究[J]. 上海交通大學學報(農(nóng)業(yè)科學版), 2001,20(1):7376.

[9]Sharp E L, Perry C R, Scharen A L, et al. Monitoring cereal rust development with a spectral radiometer [J]. Phytopathology, 1985, 75(8):936939.

[10]Hansen J G. Use of multispectral radiometry in wheat yellow rust experiments [J]. EPPO Bulletin, 1991,21:651658.

[11]黃木易, 王紀華, 黃文江, 等.冬小麥條銹病的光譜特征及遙感監(jiān)測[J]. 農(nóng)業(yè)工程學報, 2003, 19(6):154158.

[12]Moshou D, Bravo C, West J, et al. Automatic detection of ‘yellow rust’ in wheat using reflectance measurements and neural networks[J]. Computers and Electronics in Agriculture, 2004, 44:173188.

[13]冷偉鋒, 王海光, 胥巖, 等. 無人機遙感監(jiān)測小麥條銹病初探[J]. 植物病理學報, 2012, 42(2):202205.

[14]Steddom K, Heidel G, Jones D, et al. Remote detection ofRhizomaniain sugar beets [J]. Phytopathology,2003,93(6):720726.

[15]Muhammed H H, Larsolle A. Feature vector based analysis of hyperspectral crop reflectance data for discrimination and quantification of fungal disease severity in wheat[J]. Biosystems Engineering, 2003, 86(2):125134.

[16]Nicolas H. Using remote sensing to determine of the date of a fungicide application on winter wheat[J]. Crop Protection, 2004, 23(2):853863.

[17]Graeff S, Link J, Claupein W. Identification of powdery mildew (Erysiphegraminissp.tritici) and takeall disease (Gaeumannomycesgraminissp.tritici) in wheat (TriticumaestivumL.) by means of leaf reflectance measurements [J]. Central European Journal of Biology, 2006, 1:275288.

[18]Huang J F, Apan A. Detection ofSclerotiniarot disease on celery using hyperspectral data and partial least squares regression [J]. Journal of Spatial Science, 2006, 51:129142.

[19]Bauriegel E, Giebel A, Geyer M, et al. Early detection ofFusariuminfection in wheat using hyper-spectral imaging [J]. Computers and Electronics in Agriculture, 2011, 75:304312.

[20]Chen Bing, Li Shaokun, Wang Keru. Spectrum characteristics of cotton canopy infected withVerticilliumwilt and applications [J]. Agricultural Sciences in China,2008,7(5):561569.

[21]Chen Bing, Li Shaokun, Wang Keru, et al. Evaluating the severity level of cottonVerticilliumusing spectral signature analysis [J]. International Journal of Remote Sensing,2012,33(9):27062724.

[22]Cao Xueren, Luo Yong, Zhou Yilin, et al. Detection of powdery mildew in two winter wheat cultivars using canopy hyperspectral reflectance [J]. Crop Protection, 2013, 45:124131.

[23]Cao Xueren, Luo Yong, Zhou Yilin, et al. Detection of powdery mildew in two winter wheat plant densities and prediction of grain yield using canopy hyperspectral reflectance [J]. PLoS ONE, 2015, 10(3):e0121462.

[24]Martins L M, Lufinha M I, Marques C P, et al. Small format aerial photography to assess chestnut ink disease [J]. Forest, Snow and Landscape Research, 2001, 76(3):357360.

[25]Johnson D A, Alldredge J R, Hamm P B, et al. Aerial photography used for spatial pattern analysis of late blight infection in irrigated potato circles [J]. Phytopathology 2003, 93:805812.

[26]Nicolas H. Using remote sensing to determine of the date of a fungicide application on winter wheat[J]. Crop Protection, 2004, 23(2):853863.

[27]Qin Zhihao, Zhang Minghua. Detection of rice sheath blight for in-season disease management using multispectral remote sensing [J]. International Journal of Applied Earth Observation and Geoinformation, 2005, 7(2):115128.

[28]Li Xiuhua, Lee W S, Li Minzan, et al. Spectral difference analysis and airborne imaging classification for citrus greening infected trees [J]. Computers and Electronics in Agriculture, 2012, 83:3246.

[29]de Castro A I, Ehsani R, Ploetz R C, et al. Detection of laurel wilt disease in avocado using low altitude aerial imaging[J]. PLoS ONE, 2015, 10(4):e0124642.

[30]Franke J, Menz G. Multi-temporal wheat disease detection by multi-spectral remote sensing [J]. Precision Agriculture, 2007, 8:161172.

[31]張玉萍, 郭潔濱, 王爽, 等. 小麥條銹病衛(wèi)星與近地光譜反射率的比較[J]. 植物保護學報, 2009, 36(2):119122.

[32]Mirik M, Jones D C, Price J A, et al. Satellite remote sensing of wheat infected byWheatstreakmosaicvirus[J]. Plant Disease, 2011,95:412.

[33]Zhang Jingcheng, Pu Ruiliang, Yuan Lin, et al. Monitoring powdery mildew of winter wheat by using moderate resolution multi-temporal satellite imagery [J]. PLoS ONE,2014,9(4):e93107.

[34]Li Xiuhua, Leed W S, Li Minzan, et al. Feasibility study on Huanglongbing (citrus greening) detection based on WorldView-2 satellite imagery [J]. Biosystems Engineering, 2015, 132:2838.

[35]Yuan Lin, Huang Yanbo, Loraamm R W, et al. Spectral analysis of winter wheat leaves for detection and differentiation of diseases and insects [J]. Field Crops Research, 2014, 156:199207.

[36]司麗麗, 曹克強, 劉佳鵬, 等. 基于地理信息系統(tǒng)的全國主要糧食作物病蟲害實時監(jiān)測預警系統(tǒng)的研制[J]. 植物保護學報, 2006, 33(3):282286.

[37]Hijmans R J, Forbes G A, Walker T S. Estimating the global severity of potato late blight with GIS-linked disease forecast models [J]. Plant Pathology, 2000, 49:697705.

[38]馬占鴻, 石守定, 姜玉英, 等. 基于GIS 的中國小麥條銹病菌越夏區(qū)氣候區(qū)劃[J]. 植物病理學報, 2005, 34( 5) :455462.

[39]Li Boning, Cao Xueren, Chen Lin, et al. Application of geographic information systems to identify the oversummering regions ofBlumeriagraminisf.sp.triticiin China[J]. Plant Disease, 2013, 97:11681174.

[40]Nutter F W, Tylka G L, Guan J, et al. Use of remote sensing to detect soybean cyst nematode-induced plant stress [J]. Journal of Nematology, 2002, 34(3):222231.

[41]曹青, 房輝, 何有才, 等. 稻瘟病菌孢子傳播的影響因素與田間捕捉方法[J]. 信陽農(nóng)業(yè)高等?？茖W校學報, 2004, 14(3):79.

[42]Inch S, Fernando W G D, Gilbert J. Seasonal and daily variation in the airborne concentration ofGibberellazeae(Schw.) Petch spores in Manitoba [J]. Canadian Journal of Plant Pathology, 2005, 27:357363.

[43]Hirst J M. An automatic volumetric spore trap [J]. Annals of Applied Biology, 1952, 39(2):257265.

[44]周益林,段霞瑜,程登發(fā).利用移動式孢子捕捉器捕獲的孢子量估計小麥白粉病田間病情[J].植物病理學報,2007,37(3):307309.

[45]Cao Xueren, Duan Xiayu, Zhou Yilin, et al. Dynamics in concentrations ofBlumeriagraminisf.sp.triciticonidia and its relationship to local weather conditions and disease index in wheat [J]. European Journal of Plant Pathology, 2012, 132:525535.

[46]Cao Xueren, Yao Dongming, Xu Xiangming, et al. Development of weather-and airborne inoculum-based models to describe disease severity of wheat powdery mildew [J].Plant Disease,2015,99:395400.

[47]Jeger M J. Relating disease progress to cumulative numbers of trapped spores:apple powdery mildew and scab epidemics in sprayed and unsprayed orchard plots[J]. Plant Pathology, 1984, 33:517523.

[48]Xu Xiangming, Harris D C, Berrie A M. Modeling infection of strawberry flowers byBotrytiscinereausing field data[J]. Phytopathology, 2000, 90:13671374.

[49]Blanco C, Santos B D L, Romero F. Relationship between concentrations ofBotrytiscinereaconidia in air, environmental conditions, and the incidence of grey mould in strawberry flowers and fruits [J]. European Journal of Plant Pathology, 2006, 114(4):415425.

[50]Carisse O, Bacon R, Lefebvre A. Grape powdery mildew (Erysiphenecator) risk assessment based on airborne conidium concentration [J]. Crop Protection, 2009, 28:13061044.

[51]Blanco C, Santos B D L, Barrau C, et al. Relationship among concentrations ofSphaerothecamacularisconidia in the air, environmental conditions, and the incidence of powdery mildew in strawberry [J]. Plant Disease, 2004, 88(8):878881.

[52]Khan J, Qi A, Khan M F R. Fluctuations in number ofCercosporabeticolaconidia in relationship to environment and disease severity in sugar beet [J]. Phytopathology, 2009, 99:796801.

[53]Pan Zaitao, Yang Xiaobing, Pivonia S, et al. Long-term prediction of soybean rust entry into the continental United States [J]. Plant Disease, 2006, 90:840846.

[54]Wang Haiguang, Yang Xiaobing, Ma Zhanhong. Long-distance spore transport of wheat stripe rust pathogen from Sichuan, Yunnan, and Guizhou in southwestern China [J].Plant Disease,2010,94:873880.

[55]王海光, 楊小冰, 馬占鴻. 基于HYSPLIT-4模式的小麥條銹病菌遠程傳播研究[J]. 中國農(nóng)業(yè)大學學報, 2010, 15(5):5564.

[56]Singh R P,Hodson D P,Huerta-Espino J,et al.The emergence of Ug99 races of the stem rust fungus is a threat to world wheat production [J].Annual Review of Phytopathology，2011, 49:465481.

[57]Zhou Yongli. Specific and sensitive detection of the fungal pathogenUstilaginoideavirensby nested PCR [J]. Mycosystema, 2004, 23(1):102108.

[58]Foster S J, Ashby A M, Fitt B D L. Improved PCR-based assays for pre-symptomatic diagnosis of light leaf spot and determination of mating type ofPyrenopezizabrassicaeon winter oilseed rape[J]. European Journal of Plant Pathology,2002, 108:379383.

[59]Ma Zhonghua, Luo Yong, Michailides T J. Nested PCR assays for detection ofMoniliniafructicolain stone fruit orchards andBotryosphaeriadothideafrom Pistachios in California[J]. Journal of Phytopathology, 2003, 151:312322.

[60]Zeng Xiaowei, Luo Yong, Zheng Yaming, et al. Detection of latent infection of wheat leaves caused byBlumeriagraminisf.sp.triticiusing Nested PCR[J]. Journal of Phytopathology, 2009, 158:227235.

[61]Yan Jiahui, Luo Yong, Chen Tingting, et al. Field distribution of wheat stripe rust latent infection using real-time PCR [J]. Plant Disease, 2012, 96:544551.

[62]Zheng Yaming, Luo Yong, Zhou Yilin, et al. Real-time PCR quantification of latent infection of wheat powdery mildew in the field [J]. European Journal of Plant Pathology, 2013, 136(3):565575.

[63]Williams R H, Ward E, McCartney H A. Methods for integrated air sampling and DNA analysis for detection of airborne fungal spores [J]. Applied Environmental Microbiology, 2001, 67:24532459.

[64]Calderon C, Ward E, Freeman J, et al. Detection of airborne inoculum ofLeptosphaeriamaculansandPyrenopezizabrassicaein oilseed rape crops by polymerase chain reaction (PCR) assays [J]. Plant Pathology, 2002, 51:303310.

[65]Freeman J, Ward E, Calderon C, et al. A polymerase chain reaction (PCR) assay for the detection of inoculum ofSclerotiniasclerotiorum[J]. European Journal of Plant Pathology, 2002, 108:877886.

[66]Falacy J S, Grove G G, Mahaffee W F, et al. Detection ofErysiphenecatorin air samples using the polymerase chain reaction and species-specific primers [J]. Phytopathology, 2007, 97:12901297.

[67]Gent D H, Nelson M E, Farnsworth J L, et al. PCR detection ofPseudoperonosporahumuliin air samples from hop yards [J]. Plant Pathology, 2009, 58:10811091.

[68]Fraaije B A, Cools H J, Fountaine J, et al. Role of ascospores in further spread of QoI-resistant cytochrome b alleles (G143A) in field populations ofMycosphaerellagraminicola[J]. Phytopathology, 2005, 95:933941.

[69]Luo Yong, Ma Zhonghua, Reyes H C, et al. Quantification of airborne spores ofMoniliniafructicolain stone fruit orchards of California using real-time PCR [J]. European Journal of Plant Pathology, 2007, 118:145154.

[70]Cao Xueren, Yao Dongming, Zhou Yilin, et al.Detection and quantification of airborne inoculum ofBlumeriagraminisf.sp.triticiusing quantitative PCR[J/OL].European Journal of Plant Pathology,DOI:10.1007/S1065801609088.

[71]Kaczmarek J, Jêdryczka M, Fitt B D L, et al. Analyses of air samples for ascospores ofLeptosphaeriamaculansandL.biglobosaby light microscopy and molecular techniques [J]. Journal of Applied Genetics, 2009, 50(4):411419.

[72]Rogers S L, Atkins S D, West J S. Detection and quantification of airborne inoculum ofSclerotiniasclerotiorumusing quantitative PCR [J]. Plant Pathology, 2009, 58:324331.

[73]Van de Wouw A P, Stonard J F, Howlett B J, et al. Determining frequencies of avirulent alleles in airborneLeptosphaeriamaculansinoculum using quantitative PCR [J]. Plant Pathology, 2010, 59:809818.

[74]Carisse O, Tremblay D M, Lévesque C A, et al. Development of a TaqMan real-time PCR assay for quantification of airborne conidia ofBotrytissquamosaand management ofBotrytisleaf blight of onion [J]. Phytopathology, 2009, 99:12731280.

[75]Meitz-Hopkins J C, von Diest S G, Koopman T A, et al. A method to monitor airborneVenturiainaequalisascospores using volumetric spore traps and quantitative PCR [J]. European Journal of Plant Pathology, 2014,140:527541.

[76]康振生, 曹麗華, 鄭文明, 等. 小麥條銹菌條中29號生理小種SCAR檢測標記的建立[J]. 西北農(nóng)林科技大學學報(自然科學版), 2005, 33(5):5356.

[77]曹麗華, 康振生, 鄭文明, 等. 小麥條銹菌條中31號生理小種SCAR檢測標記的建立[J]. 菌物學報, 2005, 24(1):98103.

[78]Wang Baotong, Hu Xiaoping, Li Qiang, et al. Development of race specific-SCAR markers for detection of Chinese races CYR32 and CYR33 ofPucciniastriiformisf.sp.tritici[J]. Plant Disease, 2010, 94:221228.

[79]劉景梅, 陳霞, 王璧生, 等. 香蕉枯萎病菌生理小種鑒定及其SCAR標記[J]. 植物病理學報, 2006, 36(1):2834.

[80]李紅霞, 周明國, 陸悅健. 應用PCR方法檢測油菜菌核病菌對多菌靈的抗藥性[J]. 菌物系統(tǒng), 2002, 21(3):370374.

[81]Fraaije B A, Butters J A, Coelho J M, et al. Following the dynamics of strobilurin resistance inBlumeriagraminisf.sp.triticiusing quantitative allele-specific real-time PCR measurements with the fluorescent dye SYBR Green I[J]. Plant Pathology,2002, 51:4554.

[82]Luo Yong, Ma Zhonghua, Michailides T J. Quantification of allele E198A inbeta-tubulin conferring benzimidazole resistance inMoniliniafructicolausing real-time PCR [J]. Pest Management Science, 2007, 63:11781184.

[83]Yan Leiyan, Yang Qianqian, Zhou Yilin, et al. A real-time PCR assay for quantification of the Y136F allele in the CYP51 gene associated withBlumeriagraminisf.sp.triticiresistance to sterol demethylase inhibitors [J]. Crop Protection, 2009, 28:376380.

[84]Dufour M C, Fontaine S, Montarry J, et al. Assessment of fungicide resistance and pathogen diversity inErysiphenecatorusing quantitative real-time PCR assays [J]. Pest Management Science, 2011, 67:6069.

[85]曹學仁,詹浩宇,周益林,等. 電子鼻技術(shù)在快速檢測小麥矮腥黑穗病菌中的應用[J].生物安全學報, 2011,20(2):171174.

(責任編輯：田喆)

Progress in monitoring and forecasting of plant diseases

Cao Xueren1,2,Zhou Yilin1

(1. State Key Laboratory for Biology of Plant Disease and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing100193, China; 2. Key Laboratory of Integrated Pest Management on Tropical Crops, Ministry of Agriculture, Environment and Plant Protection Institute, Chinese Academy of Tropical Agricultural Sciences, Haikou571101, China)

Plant disease monitoring and forecasting are very important for disease management. This paper summarized the progress of the application of “3S” technologies (remote sensing, geographic information system and global positioning system), spore trap technology, trajectory analysis and molecular technologies in plant disease monitoring and forecasting. The strategies of future study for monitoring and forecasting of plant diseases were also discussed.

plant disease;monitoring and forecasting;“3S” technologies;spore trap;molecular technologies

20160205

公益性行業(yè)(農(nóng)業(yè))科研專項(201303016)；中央級科研院所基本科研業(yè)務(wù)費專項(2013hzs1J004)

E-mail:yilinzhou6@163.com

S 431.9

A

10.3969/j.issn.05291542.2016.03.001