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1、Team#15263Page21of23Leaves_Classification_and_Leaf_Mass_EstimationLeavesClassificationandLeafMassEstimationSummaryForthefirstproblem,weestablishourneuralnetworkmodeltoclassifyleavesoftreesbytakingeightcharacteristicsofleafintoconsideration.Theeightcharacteristicsconsistofsawtoothnumber,petiol
2、elength,bladelength,bladewidth,bladethickness,leafareaandcirculardegree.Ourresultsaresummarizedinaconclusionthatweclassifyleavesintofourteentypesincludinglinear,lanceolate,oblanceolate,spatulate,ovat,obovate,elliptic,oblong,deltoid,reniform,orbicular,peltate,perfoliateandconnate.Ourneuralnetw
3、orkimplementtheclassificationtaskreliablyandcorrectly.Forthesecondproblem,wesetupourAHPmodeltofigureoutthereasonswhyleaveshavethevariousshapesandcometoaconclusionthatgene,auxin,climateanddiseasearethemainreasonswhichleadtovariousshapes.Forthethirdproblem,wediscussthisissuefromtheperspectiveof
4、growthevolutionaryandhormones,buildcellsmechanicmodeltosolvethisproblemandsumuptheconclusionthattheshapesareinclinedtominimizeoverlappingindividualshadowsthatarecastsoastomaximizeexposure.Theshapeiseffectedbythedistributionofleaveswithinthevolumeofthetreeanditsbranches.Forthefourthproblem,weu
5、sestatisticalanalysisknowledgetoanalysethedataamongtreeprofiles,branchingstructureandleafshapes,aftermathematicallyanalyzing,finallyfindthatleavesshapeshaveadirectrelationwiththetreeprofileandbranchingstructure,Forthefifthproblem,weformulateourvolumetricmethodforleafmassestimationandlinearreg
6、ressionmodelforseekingandcomparingthecorrelationbetweentheleafmassandtreeheight,treemassandcrownvolume.Weobtainthatcrownvolumehasthehighestcorrelationwithtreeleafmass.Sowemakeuseofthecrownvolumetoestimatetheleafmass.Atlast,wewriteonepagesummarysheetofourkeyfindings.Keywords:neuralnetwork,leaf
7、classification,leafmassestimation,AHP,leafshape,volumetricmethod,linearregressionmodelI.Team#15263Page21of23Leaves_Classification_and_Leaf_Mass_EstimationContentsContents0Ⅰ.Introduction1Ⅱ.SomeDefinitions1Ⅲ.GeneralAssumptions1Ⅳ.Symbols2Ⅴ.Probl
