基于三維點(diǎn)云和改進(jìn)PointNet++的大田煙株葉片計數(shù)方法

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中圖分類號：S572；S126 文獻(xiàn)標(biāo)識碼：A文章編號：1007-5119（2025）03-0089-09

Field Tobacco Leaf Counting Method Based on 3D Point Clouds and Improved PointNet++

NAN Dewang1，LI Junying2*，LIANG Hong1， MA Erdeng2， ZHANG Hong2， XIAO Hengshu1 （1.Schoo ofInformationScience&Enginering，Yunnan UniversityKunming 650504，China; 2.YunnanAcademyoTobaco Agricultural Sciences， Kunming 650021， China）

Abstract：The leafcountof tobacco plants isoneof theimportant phenotypic parameters fortobacco leaf yield estimationTo address thechalengesof taditionalmanaltobaccoleafcounting，afieldtobaccoleafcountingmethodintegratingthre-dimensional point cloudsand improved PointNet++was proposed.This method employs UAVobliquephotographyto acquirefeld tobacco plant images and generate three-dimensional point clouds.An improved PoinNnet ++ algorithm is then utilized to perform leaf point cloud segmentation.The proposedalgorithmreplaces the MLPwith KANtoenhance leamingcapacityand minimize training loss.A DGSTD atention mechanism was proposed， which integrated DGST network and DBB multi-branch block to enhance accuracy. Additionallyarfocaloswasicooratdtodsthesibaace intouddstrutionacrosategores.allyte MeanShiftclustering algorithm wasemployed toclustertheleaf point clouds，from which theleafcount was derived.Theresults showed that the accuracy of point cloud segmentation was 92.55% ，and the mean Intersection over Union （mIoU） was 76.33% representing improvementsof06and2.81 percentagepoitsover teorgialmodel，respectively.Teproposedmethodachevesa leaf counting precision of 94.35% ， successfully implementing leaf counting of field tobacco plants in three-dimensional space.

Keywords： field tobacco plants; leaf counting; PointNet++; 3D point clouds; UAV oblique photography

煙草是以收獲葉片為目的的特殊經(jīng)濟(jì)作物，葉片數(shù)是衡量煙株生長狀況、預(yù)測產(chǎn)量的重要指標(biāo)[1-2]傳統(tǒng)人工葉片計數(shù)方法效率低、周期長，勞動強(qiáng)度大且成本高，無法滿足現(xiàn)代農(nóng)業(yè)高通量快速獲取葉片數(shù)的需求。（剩余15888字）