基于PCA與SVM的早稻稻瘟病預測方法研究.pdf

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3、話：郵編：AbstractRiceisoneofthestaplefoodcropsinChinawithawide鏟owingareacoveringaboutaquarterofthetotalfarmlandneannualriceproductionalsomakesupalmosthalfofthetotalnationalgrainoutputThericeblastisoneoftheseriousdiseasesaffe

4、ctingthehighandstableyieldsofriceInrecentdecades，thelossofplantingareashasbeenmore。thanthreemillionhectaresandthedecreaseofgrainhasalsocometotensofmillionstoneveryyearHowtoaccuratelyforecastthericeblastintimeandtakemeasu

5、restopreventpesthavealreadybeenthebasicworktocontrolthericeblastdiseaseandsecurethesafetyofficeefficiently‘1Basedonweatherconditions，thispaperstudiesonforecastingmethodsoftheearlyriceblastItmakesexperimentsinTonglingPuJi

6、weifarmAccordingtothebiologicalpropertiesandmeteorologyprincipleofriceblast，thispaperchoosestheprincipalcomponentanalysis(PCA)toanalyzethemainmeteorologicalfactorsaffectingtheoccurrenceofriceblastThroughthismethod，thepap

7、erselectsandanalyzesthemainmeteorologicalfactorsaboutthedegreeofriceblastItfindsthattherateofcontributionsofthepreviousfourmaincomponentshasalreadyreached81percentconformingtothestandardofextractingmaincomponentsByobserv

8、ingthepreviousfourmaincomponents，thispaperdiscoversthatthemeteorologicalfactors，suchasamountsanddaysofprecipitation，temperature，heliometricindex，relativehumidityandSOon，playavitalroleinthedegreeoftheoccurrenceofearlyrice

9、blastEspeciallytheamountofprecipitationasthedecisivemeteorologicalfactordirectlyinfluencestheforming，dyeingandspreadingofthebacteriaoftheearlyriceblastGenerallyspeaking，thelongandcontinuousrainydaysarethepeakperiodsofthe

10、happeningofearlyriceblastThispaperstartsfromextractingthemaincomponentsofmeteorologicalfactorsfromenvironmentbythemethodofPeAThenreferringtothelevelsofearlyriceblastineachyearthispaperusesSVMtoestablishtheforecastingmode

11、lofthedegreeoftheearlyriceblastTheauthortakesthedataofTonglingPuJiweifarmasthetrainingsamplesfrom1991t02002andfrom2005to2006，andusestheLIBSVMtoolboxofMATLABtoforecastandanalyzeAftertha_t，theauthorappliestheactualdatafrom

12、2003to2004andfrom2007to2010tochecktheforecastingefficiencyofthemodel，findingthattherateofaccuracyashighas83percentAsaresult，onthebasisofPeAandSVM，theforecastingmodelaimingatthemeteorologicalfactorsisofhighfeasibilityandq