煙苗整齊度評估分析模型研究
打開文本圖片集
（1.，南寧53000；2.，重慶400715；3.，重慶奉節(jié)404600)
中圖分類號：S572；S126文獻標識碼：A
文章編號：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)濟作物，煙苗整齊度是煙草生產(chǎn)的關鍵指標之一，直接影響田間管理效果。（剩余23225字）