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1、LeavesClassificationandLeafMassEstimationSummaryForthefirstproblem,weestablishourneuralnetworkmodeltoclassifyleavesoftreesbytakingeightcharacteristicsofleafintoconsideration.Theeightcharacteristicsconsistofsawtoothnumber,petiolelength,bladelength,bladewidth,bladethickness,leafareaandcircu
2、lardegree.Ourresultsaresummarizedinaconclusionthatweclassifyleavesintofourteentypesincludinglinear,lanceolate,oblanceolate,spatulate,ovat,obovate,elliptic,oblong,deltoid,reniform,orbicular,peltate,perfoliateandconnate.Ourneuralnetworkimplementtheclassificationtaskreliablyandcorrectly.Fort
3、hesecondproblem,wesetupourAHPmodeltofigureoutthereasonswhyleaveshavethevariousshapesandcometoaconclusionthatgene,auxin,climateanddiseasearethemainreasonswhichleadtovariousshapes.Forthethirdproblem,wediscussthisissuefromtheperspectiveofgrowthevolutionaryandhormones,buildcellsmechanicmodelt
4、osolvethisproblemandsumuptheconclusionthattheshapesareinclinedtominimizeoverlappingindividualshadowsthatarecastsoastomaximizeexposure.Theshapeiseffectedbythedistributionofleaveswithinthevolumeofthetreeanditsbranches.Forthefourthproblem,weusestatisticalanalysisknowledgetoanalysethedataamon
5、gtreeprofiles,branchingstructureandleafshapes,aftermathematicallyanalyzing,finallyfindthatleavesshapeshaveadirectrelationwiththetreeprofileandbranchingstructure,Forthefifthproblem,weformulateourvolumetricmethodforleafmassestimationandlinearregressionmodelforseekingandcomparingthecorrelati
6、onbetweentheleafmassandtreeheight,treemassandcrownvolume.Weobtainthatcrownvolumehasthehighestcorrelationwithtreeleafmass.Sowemakeuseofthecrownvolumetoestimatetheleafmass.Atlast,wewriteonepagesummarysheetofourkeyfindings.Keywords:neuralnetwork,leafclassification,leafmassestimation,AHP,leaf
7、shape,volumetricmethod,linearregressionmodelI.ContentsContents0Ⅰ.Introduction1Ⅱ.SomeDefinitions1Ⅲ.GeneralAssumptions1Ⅳ.Symbols2Ⅴ.Problemanalysis2Ⅵ.Models36.1Neuralnetworkmodeltoclassifytreeleaves36.1.1Neuromime36.1.2Multi-layerperceptronnetwork46.1.3Back-propogation
