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1、Team#15263Page23of25Leaves_Classification_and_Leaf_Mass_EstimationLeavesClassificationandLeafMassEstimationSummaryForthefirstproblem,weestablishourneuralnetworkmodeltoclassifyleavesoftreesbytakingeightcharacteristicsofleafintoconsideration.Theeightcharacteristicsconsistofsawtoothnumber,pe
2、tiolelength,bladelength,bladewidth,bladethickness,leafareaandcirculardegree.Ourresultsaresummarizedinaconclusionthatweclassifyleavesintofourteentypesincludinglinear,lanceolate,oblanceolate,spatulate,ovat,obovate,elliptic,oblong,deltoid,reniform,orbicular,peltate,perfoliateandconnate.Ourne
3、uralnetworkimplementtheclassificationtaskreliablyandcorrectly.Forthesecondproblem,wesetupourAHPmodeltofigureoutthereasonswhyleaveshavethevariousshapesandcometoaconclusionthatgene,auxin,climateanddiseasearethemainreasonswhichleadtovariousshapes.Forthethirdproblem,wediscussthisissuefromthep
4、erspectiveofgrowthevolutionaryandhormones,buildcellsmechanicmodeltosolvethisproblemandsumuptheconclusionthattheshapesareinclinedtominimizeoverlappingindividualshadowsthatarecastsoastomaximizeexposure.Theshapeiseffectedbythedistributionofleaveswithinthevolumeofthetreeanditsbranches.Forthef
5、ourthproblem,weusestatisticalanalysisknowledgetoanalysethedataamongtreeprofiles,branchingstructureandleafshapes,aftermathematicallyanalyzing,finallyfindthatleavesshapeshaveadirectrelationwiththetreeprofileandbranchingstructure,Forthefifthproblem,weformulateourvolumetricmethodforleafmasses
6、timationandlinearregressionmodelforseekingandcomparingthecorrelationbetweentheleafmassandtreeheight,treemassandcrownvolume.Weobtainthatcrownvolumehasthehighestcorrelationwithtreeleafmass.Sowemakeuseofthecrownvolumetoestimatetheleafmass.Atlast,wewriteonepagesummarysheetofourkeyfindings.Key
7、words:neuralnetwork,leafclassification,leafmassestimation,AHP,leafshape,volumetricmethod,linearregressionmodelI.Team#15263Page23of25Leaves_Classification_and_Leaf_Mass_EstimationContentsContents0Ⅰ.Introduction1Ⅱ.SomeDefinitions1Ⅲ.GeneralAssumptions1Ⅳ.Symbols2Ⅴ.Probl
