基于無人機(jī)多光譜和改進(jìn)BPNN的煙草病毒病檢測(cè)

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中圖分類號(hào)：S572；S126 文獻(xiàn)標(biāo)識(shí)碼：A文章編號(hào)：1007-5119（2025）03-0098-11

Tobacco Virus Disease Detection Based on UAV Multispectral and Improved BPNN

ZHANG Yangliang12， JIANG Xueyan1， LI Min2， JIANG Houlong3， JIANG Lianqiang4， GUO Leifeng2*，WANG Xinweil

（1.KeyLaboratoryofTobaco PestMonitoring &Integrated Management/Istitute ofTobaccoResearchofCAAS，Qingdao66101，

China;2.AgricuturalInfomationItuteofCeseAcademyofAgriculturalSienes，BeiingO1Chia;3.obaccoLeaf

Company，ChongingBrachf，hongingo，Cina;4.ingshanBrachoficuanobaccoopaniaa 615000， Sichuan， China）

Abstract：Thisstudyaims toidentifytobacovirus diseasebyintegrating UAV-based multispectralremotesensing technologywith animprovedBPneuralnetwork （BPNN）.Multispectral imagesof healthyanddiseasedtobacoplants withvaryingdegreesof virus infectionwerecapturedusingtheDJIP4MUAV.Atotalof19 vegetation indiceswerecalculated toconstructfeaturesets for correlationanalysis.K-nearest neighbors （KNN），random forest （RF），supportvectormachine （SVM），traditional BPNN，and improved BPNN were used toperformcomparative testsonbinaryandtermaryclasificationsamples.Theimproved BPNN，with optimizationsinetworktructure，imbalanceatahandling，actiationfunctioneplacing，andotimzerancementchied 89% accuracyand anF1 scoreof O.88 forbinaryclassification， and 79% accuracywith anF1 scoreof O.76 for ternaryclassification-both outperforming traditionalalgorithms.TheseresultsindicatethatUAVmultispectraldatacombinedwithanimprovedBPNNholds applicationpotential forthedetectionof tobaccovirusdisease，providing technical supportforearlywamingand preventionof agricultural diseases.

Keywords： tobacco; virus disease; UAV; multispectral; improved BPNN

煙草病毒病是煙草生產(chǎn)過程中最常見且最具破壞性的病害，常年造成巨大經(jīng)濟(jì)損失[1]。（剩余17473字）