自行式割草机的设计传动部件、罩壳部件设计（含CAD图纸）
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自行
割草机
设计
传动
部件
罩壳
cad
图纸
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COMPUTERSANDELECTRONICSINAGRICULTURE362002215223COMPUTERVISIONBASEDSYSTEMFORAPPLESURFACEDEFECTDETECTIONQINGZHONGLIA,MAOHUAWANGB,WEIKANGGUA11DEPARTMENTOFINFORMATIONANDELECTRONICENGINEERING,ZHEJIANGUNIVERSITY,HANGZHOU,PEOPLESREPUBLICOFCHINABRESEARCHCENTREFORPRECISIONAGRICULTURE,CHINAAGRICULTURALUNIVERSITY,BEIJING,PEOPLESREPUBLICOFCHINAABSTRACTANOVELAUTOMATEDAPPLESURFACEDEFECTSORTINGEXPERIMENTALSYSTEMBASEDONCOMPUTERIMAGETECHNOLOGYHASBEENDEVELOPEDTHEHARDWARESYSTEMHASTHEADVANTAGEOFBEINGABLETOINSPECTSIMULTANEOUSLYFOURSIDESOFEACHAPPLEONTHESORTINGLINETHEMETHODS,INCLUDINGIMAGEBACKGROUNDREMOVAL,DEFECTSSEGMENTATIONANDIDENTIFICATIONFORSTEMENDANDCALYXAREAS,WEREDEVELOPEDTHERESULTSSHOWTHATTHEEXPERIMENTALHARDWARESYSTEMISPRACTICALANDFEASIBLE，ANDTHATTHEPROPOSEDALGORITHMOFDEFECTDETECTIONISEFFECTIVE2002ELSEVIERSCIENCEBVALLRIGHTSRESERVEDKEYWORDSMACHINEVISIONAPPLESURFACEDEFECT1INTRODUCTIONCHINAISALARGEAGRICULTURALCOUNTRYITSANNUALAPPLEPRODUCTIONISOVER17MILLIONTONSMUCHOFTHESORTINGANDGRADINGPROCESS,HOWEVER,ISSTILLNOTAUTOMATEDHANDINSPECTIONOFFRUITISTEDIOUSANDCANCAUSEEYEFATIGUEITISALSOSUBJECTTOSORTINGERRORSDUETODIFFERENTJUDGMENTBYDIFFERENTPERSONSALTHOUGHSOMEQUALITYINSPECTIONPROCEDURESSUCHASCOLOR,SIZE,ANDSHAPEAREPERFORMEDBYAUTOMATEDSYSTEMSINWESTERNCOUNTRIES,THEAUTOMATIONOFTHEDEFECTSORTINGPROCESSISSTILLACHALLENGINGSUBJECTDUETOTHECOMPLEXITYOFTHEPROBLEMCURRENTLYTHEREARETWOMAINPROBLEMSBLOCKINGTHEIMPLEMENTATIONOFAUTOMATICAPPLEGRADINGONEISHOWTOACQUIRETHEWHOLESURFACEIMAGEOFANAPPLEBYCAMERASATANONLINESPEEDTHEOTHERISHOWTOQUICKLYIDENTIFYTHESTEMANDCORRESPONDINGAUTHOREMAILADDRESSMHWPUBLICBTANETCNMWANG01681699/02/SEEFRONTMATTER2002ELSEVIERSCIENCEBVALLRIGHTSRESERVEDPIIS0168169902000935216QLIETAH/COMPUTERSCMDELECTRONICSINAGRICULTURE362002215223CALYXTOSOLVETHEFIRSTPROBLEM,GROWEANDDELWICHE1996,TAO1996USEDAROLLERCONVEYERSYSTEMTHEDRAWBACKOFTHISMETHODWASTHATTHECAMERAABOVETHECONVEYORCANNOTINSPECTTHETWOENDSIDESOFTHEHORIZONTALAXESOFTHEROLLINGFRUITSFORTHESECONDPROBLEM,THROOPETAL1997DEVELOPEDTWOKINDSOFORIENTINGDEVICESTHESEDEVICESWEREUSEDTOROTATEAPPLESOFDIFFERENTVARIETIESALONGTHESTEMCALYXAXESBUTTHERESULTSSHOWEDTHATTHEVARIETIESTHATWERESUCCESSFULLYORIENTEDWITHONESYSTEMWOULDNOTORIENTUSINGTHEOTHERDEVICEYANG1993USEDSTRUCTUREDLIGHTINGTOIDENTIFYTHESTEMANDCALYXOFAPPLESTHEMAJORPROBLEMWITHTHESTRUCTUREDLIGHTINGISTHEMISCLASSIFICATIONOFLASERLINESONTHEIMAGEWENAADTAO1998SUCCESSFULLYDEVELOPEDADUALCAMERANIR/MIRIMAGINGMETHODFORAPPLEDEFECTRECOGNITIONANDSTEMCALYXIDENTIFICATIONBUTTHEMIRCAMERAISTOOEXPENSIVETOUSEINCHINATHEOBJECTIVEOFTHEWORKDESCRIBEDINTHISPAPERWASTODEVELOPANEXPERIMENTALSYSTEMTHATCANINSPECTFOURSIDESOFEACHAPPLE，SIMULTANEOUSLY,ATONLINETHROUGHPUTOVERTHREETOFOURFRUITSPERSANDTHECORRESPONDINGMETHODSFOREFFECTIVEDEFECTSSEGMENTATIONANDRECOGNITION2SYSTEMSETUPOVERVIEWASYSTEMCAPABLEOFINSPECTINGFOURDIRECTIONSOFEACHAPPLEATONLINETHROUGHPUTWASDEVELOPEDTHESETUPOFTHESYSTEMISSHOWNINFIG1ITCONSISTEDOFAFEEDINGUNIT，ANAPPLEUNIFORMSPACINGUNIT，AMACHINEVISIONSYSTEM，ANDASORTINGCONVEYORTHEBASICFEEDINGCONVEYORTRANSPORTEDTHEAPPLESTOTHEUNIFORMSPACINGCONVEYORTHEN,THEAPPLESWEREFEDTOTHEMACHINEVISIONSYSTEMFORTHEDEFECTQLIETAL/COMPUTERSANDELECTRONICSINAGRICULTURE362002215223217INSPECTIONFINALLY，THEAUTOMATICSORTINGUNITACCOMPLISHEDTHEAPPLEGRADINGOPERATIONTHEMACHINEVISIONSYSTEMINCLUDEDACUPTYPECONVEYOR,ALIGHTINGCHAMBERFORTHEDESIREDSPECTRUMANDLIGHTDISTRIBUTIONFORFRUITILLUMINATION,TWOCAMERAS,ANDANIMAGEGRABBINGCARDWITHFOURINPUTCHANNELSINSERTEDINAMICROCOMPUTERPROCESSORSPEED500MHZASANEXPERIMENTALSYSTEM,THEFRUITFEEDINGSYSTEMANDTHEAUTOMATICSORTINGSYSTEMWERENOTCONSTRUCTEDINTHEFIRSTSTAGEOFTHERESEARCHTOACHIEVEABASICALLYCOMPLETEINSPECTIONOFAPPLESONTHEFRUITSORTINGLINE，TWOIDENTICALMONOCHROMATICCAMERASWEREMOUNTEDABOVEANDBELOWTHECONVEYOR,RESPECTIVELYTHESETUPOFTHEVISIONSYSTEMISSHOWNINFIG2THEIMAGESENSORSINTHECAMERASHADANACTUALRESOLUTIONOF580HORIZONTALAND350VERTICALTVLINESEACHCAMERAWASSYNCHRONIZEDTOANOTHERTIMINGSOURCEANDHADAVARIABLEELECTRONICSHUTTERIDENTICAL85MMFOCALLENGTHCMOUNTLENSESWEREATTACHEDTOTHECAMERAS，WITHINTERFERENCEBANDPASSOPTICALFILTERS840NMATTACHEDTOTHEOUTSIDEOFEACHLENSTHECONVEYORWASCOMPOSEDOFFRUITCUPSWITHOUTBOTTOMSASSHOWNINFIG2TWOMIRRORSWEREFIXEDONBOTHSIDESOFTHECONVEYORTHUSTHECAMERAABOVETHECONVEYORTOOKTHREESIDEVIEWSOFANAPPLE,IETOPANDTWOSIDESTHECAMERABELOWTHECONVEYORTOOKABOTTOMVIEWOFTHEFRUITMOREOVER,THISIMAGINGSYSTEMWASABLETOINSPECTSEVERALAPPLESONTHECONVEYORSIMULTANEOUSLYTHISSCHEMEHADTHEADVANTAGEOFBEINGABLETOINSPECTSIMULTANEOUSLYFOURSIDESOFEACHAPPLEWHILEITWASTRAVELINGONTHECONVEYOR3ALGORITHMDESCRIPTIONTHEALGORITHMDEVELOPEDFORTHESURFACEDEFECTDETECTIONMAINLYINCLUDEDMODULESOFIMAGEPREPROCESSING,DEFECTSEGMENTATION,STEMCALYXRECOGNITION,ANDDEFECTAREACALCULATIONANDGRADINGTHEALGORITHMISSHOWNSCHEMATICALLYINFIG331IMAGEBACKGROUNDREMOVALTHROUGHAMETHODOFSUBTRACTIONTHEIMAGEBACKGROUNDSINTHEMIRRORANDONTHECONVEYORWEREDIFFERENT,SOITWASIMPOSSIBLETOSEGMENTTHEPARTSOFFRUITBYASIMPLETHRESHOLDPROCESSTHEREFORE,ASUBTRACTINGMETHODWASUSED,ASDEPICTEDBELOW218QLIETAL/COMPUTERSANDELECTRONICSINAGRICULTURE362002215223WHEREGX,YISTHEIMAGEAFTERITSBACKGROUNDHASBEENREMOVED,FX,Y）ISTHEORIGINALIMAGE,BX,YISTHEBACKGROUNDIMAGE,ANDTISTHETHRESHOLD32DEFECTSSEGMENTATIONBYUSINGREFERENCEAPPLEIMAGESAPPLESUNDERINSPECTIONHADSUBSTANTIALLYSPHERICALSHAPES,RESULTINGINCURVEDDISTRIBUTEDIMAGEINTENSITYTHISCURVEDDISTRIBUTIONCAUSEDTHEINTENSITYVALUESOFTHENORMALSURFACENEARTHEBOUNDARYTOBELOWERTHANTHEINTENSITYOFTHEDEFECTPATCHESONTHESURFACEOFTHEFRUITITISDIFFICULTTOUSEANYSIMPLEGLOBALTHRESHOLDSEGMENTATIONALGORITHMFORDEFECTEXTRACTIONLOCALADAPTIVEMETHODSCOULDBEUSEDFORDEFECTSEGMENTEXTRACTIONHOWEVER,THEPROCESSINGTIMEPREVENTSTHEIRPRACTICALUSEINREALTIMEFRUITSORTINGOPERATIONSBASEDONTHEREFERENCEIMAGEOFANAPPLE,LIANDWANG1999DEVELOPEDAMETHODTOACCOMPLISHDEFECTSEGMENTATIONFORACURVEDFRUITIMAGEINTHISMETHOD,AREFERENCEFRUITIMAGERFIWASGENERATEDFIRSTANDTHEORIGINALFRUITIMAGEFORINSPECTIONWASTHENNORMALIZEDQLIETAL/COMPUTERSANDELECTRONICSINAGRICULTURE362002215223219TOACHIEVETHENORMALIZEDREFERENCEFRUITIMAGENRFIFINALLYBYSUBTRACTINGNORMALIZEDORIGINALFRUITIMAGENOFIFROMTHENRFIANDTHENBYSIMPLETHRESHOLDPROCESSING,THEDEFECTSCOULDBEEXTRACTEDEASILY33STEMCALYXIDENTIFICATIONBASEDONFRACTALFEATURESANDARTIFICIALNEURALNETWORKDURINGTHEDEFECTINSPECTIONPROCESS,ITISDIFFICULTTODISTINGUISHTHESTEMANDCALYXFROMTRUEDEFECTS，BECAUSETHEYARESIMILARTODEFECTIVESPOTSINTHEIMAGEBASEDONFRACTALDIMENSIONSANDNEURALNETWORKSNN,THEAUTHORSOFTHISPAPERDEVELOPEDANOVELMETHODTODISTINGUISHTHESTEMCALYXCONCAVEAREAFROMTRUEDEFECTS220QLIETAL/COMPUTERSANDELECTRONICSINAGRICULTURE362002215223FRACTALISATERMUSEDTODESCRIBETHESHAPEANDAPPEARANCEOFTHEOBJECTS,WHICHHAVETHEPROPERTIESOFSELFSIMILARITYANDSCALEINVARIANCEFRACTALDIMENSIONISASCALEINDEPENDENTMEASUREOFTHEDEGREEOFSURFACEROUGHNESSORBOUNDARYIRREGULARITYALTHOUGHTHEINTENSITYOFSTEMCALYXANDTRUEDEFECTSARESIMILAR,THEIRFRACTALFEATURESMAYBEDIFFERENTMOREOVER，FRACTALANALYSISINTHEFREQUENCYDOMAINONLYDEPENDSONTHEFREQUENCYDISTRIBUTIONOFTHEIMAGESURFACETHESEFRACTALTEXTURALFEATURESWOULDBEINDEPENDENTOFTHEVARIATIONOFAMBIENTLIGHTINTENSITYANDORIENTATIONOFTHEAPPLESBEINGSORTEDSOTHISMETHODISSUITABLEFORAPPLESORTINGOPERATIONSWHEREAPPLESAREINRANDOMORIENTATIONSTHEIMAGEDISTRIBUTIONCANBEREGARDEDASATHREEDIMENSIONALCURVEDSURFACEBASEDONTHEABOVECONSIDERATION,FIVEFRACTALDIMENSIONSINCLUDINGONETRADITIONALFRACTALDIMENSIONANDFOURORIENTEDFRACTALDIMENSIONSWERESELECTEDASTHEFEATURESOFTHEIMAGESPOTSPRODUCEDBYSTEMCALYXCONCAVEAREAORTRUEDEFECTSTHEFOURORIENTEDFRACTALDIMENSIONSD1,D12,D3,D4ARESHOWNINFIG4INFACT，THEORIENTEDFRACTALDIMENSIONSWERETHEFRACTALDIMENSIONSOFTHECURVESINTHECORRESPONDINGDIRECTIONSFIG5THEFIVEFRACTALDIMENSIONSARECALCULATEDBYTHEMETHODDERIVEDBYLIANDWANG2000THEDIGITALIMAGECANBEDEPICTEDASZFX,Y,WHEREX，YISTHECOORDINATESOFAPIXELZISTHEGRAYVALUEASSUMINGTHEAREAOFTHEIMAGEISMXMTHEXYPLANEOFTHEIMAGEISDIVIDEDINTOGRIDSWITHAREATHEMAXIMUMANDTHEMINIMUMOFGRAYVALUESINTHEGRIDAREEXPRESSEDASUI,JANDBI,J，RESPECTIVELYANDTHEIRDIFFERENCEISDUI,JBI,JTHENTHETOTALNONEMPTYBOXNUMBERNFORALLTHEGRIDSISCALCULATEDASFORALLTHEGIVENADATASETFROMASERIESOFPOINTSLOG,LOGNCANBEOBTAINEDTHROUGHLINEARREGRESSIONOFTHEPOINTSLOG,LOGN,THEMINUSSLOPEOFTHEREGRESSIONLINEGIVESTHEESTIMATEDFRACTALDIMENSIONTHEFOURORIENTEDDIMENSIONSCANBEESTIMATEDBYUSINGASIMILARMETHODAFEEDFORWARDBACKPROPAGATIONBPNNALGORITHMWASUSEDTOCLASSIFYSTEMCALYXFROMTRUEDEFECTAREASTHEFEEDFORWARDNETWORKSTRUCTUREWASSUITABLEFORHANDLINGNONLINEARRELATIONSHIPSBETWEENINPUTANDOUTPUTVARIABLESOFPREDICTIOND4D3D2QLIETAL/COMPUTERSANDELECTRONICSINAGRICULTURE362002215223221222QLIETAL/COMPUTERSANDELECTRONICSINAGRICULTURE362002215223FIG5ORIENTEDFRACTALCURVEHIDDENLAYERRELATEDPROBLEMSTHEDESIGNEDBPNETWORKISSHOWNINFIG6THENNMODELHADFIVEINPUTNODES,ONEHIDDENLAYERWITHFOURHIDDENNODES，ANDONEOUTPUTNODEDURINGTHETRAININGPROCESS,THEWEIGHTSOFTHENETWORKWEREUPDATEDAFTEREACHPASSTHROUGHALLTHETRAININGSAMPLESTHECONVERGENCEOFTHELEARNINGWASJUDGEDBYTWOCONDITIONSWHETHERTHEMEANSQUAREDERRORFORALLTRAININGSAMPLESWERESMALLERTHANATOLERANCEVALUE,ANDWHETHERTHEOUTPUTERRORSFOREACHTRAININGSAMPLEWERESMALLERTHANANOTHERPREDEFINEDTOLERANCEVALUE34REALTIMEIMPLEMENTATIONOFAPPLESURFACEDEFECTDETECTIONTHEREALTIMEIMPLEMENTATIONOFAPPLESURFACEDEFECTDETECTIONISDIVIDEDINTOTWOSTAGESTHEFIRSTISTHESEGMENTATIONOFDOUBTFULSPOTAREAS,INCLUDINGDEFECTSANDSTEMCALYXAREAS，BYTHEMETHODDESCRIBEDINSECTION32THESEGMENTATIONRESULTSSHOWTHATTHESTEMCALYXAREASAREOFTENWITHBIGGERAREASSOINTHESECONDSTAGE,THESEGMENTEDSPOTSWITHAREASBIGGERTHANAGIVENVALUEAREFURTHERPROCESSEDFORDISTINGUISHINGSTEMCALYXCONCAVEAREASFROMDEFECTSBYTHEMETHODPRESENTEDINSECTION334TESTSANDRESULTSTHEALGORITHMWASUSEDTODETECTDEFECTSANDSTEMCALYXAREASINFORTYSAMPLESOFFUJIAPPLESSOMERESULTSARESHOWNINFIG7，WHEREA,C,E,ANDGARETHEQLIETAL/COMPUTERSANDELECTRONICSINAGRICULTURE362002215223223ORIGINALIMAGEOFTHEAPPLESTOBEINSPECTED,ANDB,D，（F，ANDHARETHEDEFECTSEGMENTATIONRESULTSTHESERESULTSSHOWTHATTHEDEFECTSANDSTEMCALYXAREASWEREBASICALLYEXTRACTEDTHESEGMENTEDSPOTSWITHAREABIGGERTHANAGIVENVALUEWEREFURTHERPROCESSEDFORDISTINGUISHINGSTEMCALYXCONCAVEAREAFROMDEFECTSBYTHEMETHODINSECTION33TABLE1LISTSSOMERESULTSOFTHESTEMCALYXRECOGNITIONBYTHEBPNETWORKIFTHEOUTPUTVALUEOFTHENETWORKISNEAR1，THEDETECTEDPATCHISTHESTEMCALY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经网络34苹果表面缺陷的即时检查苹果表面缺陷的即时检查分为两个阶段。第一阶段是32中描述的方法对可疑缺陷区域的分割（包括茎、萼和缺陷表面），其结果显示莲萼区域时常为较大的区域。在第二阶段中，对较大的分割区域进一步处理以区别真正的表面缺陷。4实验和结果运算法则被用于探测四个富士苹果的表面缺陷和茎萼区域。结果如图7，A、C、E和（G是苹果的原始探测图像。（B、（D、（F和（H是缺陷区域分割后的结果。这些结果显示缺陷区域和茎萼区域基本上被提取出来，大于指定值的分割区域被进一步分割。表1为BP对茎萼区域的识别结果。如果神经网络的输出值接近1，则为茎萼区域。如果神经网络的输出值接近0，则为真实的缺陷区域。实验结果显示不规则碎片形状对正常的水果凹入
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