煙苗整齊度評(píng)估分析模型研究

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（1.，南寧53000；2.，重慶400715；3.，重慶奉節(jié)404600)

中圖分類(lèi)號(hào)：S572；S126文獻(xiàn)標(biāo)識(shí)碼：A

文章編號(hào)：1007-5119（2025）02-0101-12

An Analytical Model for Assessing Tobacco Seedling Uniformity

LI Qunling1, SUN Jiazhao, WANG Zhenguo3, CHENG ,RAN Yu'ao2, LI ,DING Wei1*

(1.ChinaTobaoagxistrialCoLtd.,ago,Cina;2.CllgeoantrotectioouthwstUiy

Chongqing 40o715,China;3.FengjieBranchofChongqingTobacoCompany,ChinaNationalTobacco Corporatio,Fengie 404600, Chongqing, China)

Abstract:Toacheverapidassessmentandeficentanalysisoftheuniforityoftobaccosedlings inintensiveedlingfactorythis studyemploysageneralizedaditive model(GAM)toanalyetobaccoseed nurserydataadscrnfor idicatorsoftobaccoseedling uniformity. We evaluatedRandomForest algorithm,BPNeuralNetworkalgorithm,and Support VectorMachine(SVM)algorithm. Particle Swarm Optimization (PSO)isthen appliedtooptimizeeachof these modelsseparately.Thisstudyconstructed image recognitionmodelsforassessingtheuniforityof tobaccseednurseryusingdeepleaingalgorithms,twooptimizers,Adamand Nadam,specificallyAlexNet,ResNet-10l,and GoogleNet.Teresearchresultsindicated thattheplantheight,stemcicuferee, and numberofefectiveleaves of tobaccoseedlings hadasignificantimpactontheuniformityofthe tobaccoseedlings.ThePartcle Swarm Optimized Random Forest model demonstrates the best performance, with accuracy of 8 8 . 0 0 % valueof O.69,andMean AbsoluteError(MAE）of 0.13.TheAdam-GoogLeNet modelshows thebestrecognition performance,averagingacuracyof 9 3 . 8 9 % Overall,findings of thisstudy provideareasonable basis for thescientificevaluationoftobacco nurserybeduniformityand offer support for the development of tobacco seedling uniformity image recognition systems.

Keywords: tobacco seedbed; uniformity index; deep learning; uniformity model; image recognition

煙草是一種重要的經(jīng)濟(jì)作物，煙苗整齊度是煙草生產(chǎn)的關(guān)鍵指標(biāo)之一，直接影響田間管理效果。（剩余23225字）