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1、Team#15263Page21of23Leaves_Classification_and_Leaf_Mass_EstimationLeavesClassificationandLeafMassEstimationSummaryForthefirstproblem,weestablishourneuralnetworkmodeltoclassifyleavesoftreesbytakingeightcharacteristicsofleafintoconsideration.Theeightcharacteristicsconsistofsawtoothnumber,petio
2、lelength,bladelength,bladewidth,bladethickness,leafareaandcirculardegree.Ourresultsaresummarizedinaconclusionthatweclassifyleavesintofourteentypesincludinglinear,lanceolate,oblanceolate,spatulate,ovat,obovate,elliptic,oblong,deltoid,reniform,orbicular,peltate,perfoliateandconnate.Ourneuralne
3、tworkimplementtheclassificationtaskreliablyandcorrectly.Forthesecondproblem,wesetupourAHPmodeltofigureoutthereasonswhyleaveshavethevariousshapesandcometoaconclusionthatgene,auxin,climateanddiseasearethemainreasonswhichleadtovariousshapes.Forthethirdproblem,wediscussthisissuefromtheperspectiv
4、eofgrowthevolutionaryandhormones,buildcellsmechanicmodeltosolvethisproblemandsumuptheconclusionthattheshapesareinclinedtominimizeoverlappingindividualshadowsthatarecastsoastomaximizeexposure.Theshapeiseffectedbythedistributionofleaveswithinthevolumeofthetreeanditsbranches.Forthefourthproblem
5、,weusestatisticalanalysisknowledgetoanalysethedataamongtreeprofiles,branchingstructureandleafshapes,aftermathematicallyanalyzing,finallyfindthatleavesshapeshaveadirectrelationwiththetreeprofileandbranchingstructure,Forthefifthproblem,weformulateourvolumetricmethodforleafmassestimationandline
6、arregressionmodelforseekingandcomparingthecorrelationbetweentheleafmassandtreeheight,treemassandcrownvolume.Weobtainthatcrownvolumehasthehighestcorrelationwithtreeleafmass.Sowemakeuseofthecrownvolumetoestimatetheleafmass.Atlast,wewriteonepagesummarysheetofourkeyfindings.Keywords:neuralnetwor
7、k,leafclassification,leafmassestimation,AHP,leafshape,volumetricmethod,linearregressionmodelI.Team#15263Page21of23Leaves_Classification_and_Leaf_Mass_EstimationContentsContents0Ⅰ.Introduction1Ⅱ.SomeDefinitions1Ⅲ.GeneralAssumptions1Ⅳ.Symbols2Ⅴ.Probl
