外文翻译原文-基于机器视觉的苹果果实识别

AdvanceJournalofFoodScienceandTechnology2(6):325-327,2010ISSN:2042-4876MaxwellScientificOrganization,2010Submitteddate:October22,2010Accepteddate:November13,2010Publisheddate:November30,2010CorrespondingAuthor:M.B.Lak,DepartmentofAgriculturalMechanization,ScienceandResearchBranch,IslamicAzadUniversity,Tehran,Iran325AppleFruitsRecognitionUnderNaturalLuminanceUsingMachineVision1M.B.Lak,2S.Minaei,3J.Amiriparianand2B.Beheshti1DepartmentofAgriculturalMechanization,2DepartmentofAgriculturalMachineryEngineering,ScienceandResearchBranch,IslamicAzadUniversity,Tehran,Iran3DepartmentofMechanicsofAgriculturalMachinery,FacultyofAgriculture,Bu-AliSinaUniversity,Hamedan,IranAbstract:Inthisstudy,edgedetectionandcombinationofcolorandshapeanalyseswasutilizedtosegmentimagesofredapplesobtainedundernaturallighting.Thirtyimageswereacquiredfromanorchardinordertofindanappleineachimageandtodetermineitslocation.Twoalgorithms(edgedetection-basedandcolor-shapebased)weredevelopedtoprocesstheimages.Theywerefiltered,convertedtobinaryimages,andnoise-reduced.Edgedetectionbasedalgorithmwasnotsuccessful,whilecolor-shapebasedalgorithmcoulddetectapplefruitsin83.33%ofimages.Keywords:Appleharvesting,color-shapebasedalgorithm,edgedetection,machinevisionINTRODUCTIONFreshfruitsharvestingisasensitiveoperation.Itsprofitabilitymaybeinfluencedbylaborinaptitude,costsandunavailability,lowqualityharvesting,andoperationuntimeliness.So,mechanizedharvestingoperationmaysolvetheproblems.MechanizationofapplefruitsharvestingincountrieslikeIranthatisthe4thappleproducerintheworld(FAO,2009)isanessentialneed.Mechanizedfruitharvestingmaybemechanicallyorautomatically.Problemsaccompanyingwithmechanicalharvestingresultedindevelopmentofroboticharvestingmethods,therebyprototypemachinevisionbasedharvestershasbeenincreasinglybeingdeveloped.ParrishandGoksel(1977)andBulanonandKataoka(2010)studiedroboticapplefruitharvesting.Theautomatedharvestingsystemshouldperformthefollowingoperations:(1)recognizeandlocatethefruit;(2)reachforthefruit;(3)detachthefruitwithoutcausingdamagebothtothefruitandthetree;and(4)moveeasilyintheorchard(Sarig,1990).Thefirstoperationneedsdevelopmentofappropriatemethodstodetectandlocatethefruits.Usingphotometricinformationbased(SchertzandBrown,1968)andinfraredlaserrangefinding(Jimenezetal.,2000)methodsweredeveloped.While,imageprocessingbasedmethodshavebeenusedtodetectandlocatedthefruits(BulanonandKataoka,2010;Satish,2007;Harreletal.,1989).Bothintensity/colorpixel-basedandshape-basedanalysismethodswereappropriatestrategiesfortherecognitionoffruits,butsomeproblemsarosefromthevariabilityofthesensedimageitselfwhenusingCCDcameras,whichareverysensitivetochangesinsunlightintensityaswellasshadowsproducedbytheleaves(Jimenezetal.,2000).SincenoresearchhasbeenreportedonroboticappleharvestinginIran,thispaperfocusesonrecognitionofapples,asthefirststageofappleroboticharvesting.Recognitionofapplefruitsusingmachinevisionundernaturaldaylightconditionswastheobjectiveofthisstudy.ThirtyimagesofRedDeliciousapplecanopywereselectedrandomlyfromphotostakenofappletreesinautumn.TheimagesweretakenfromHamedangroves,inIran.MATERIALSANDMETHODSThirtydigitalimageswereobtainedunderuncontrolleddaylightconditions.Imageframeswere30722304pixelsintheJPEGformat.Adigitalcamera(Sony,DSC-H5,ColorCCDCamera)wasusedtoacquiretheRGBimages.Imageprocessingalgorithm:Thegoalwasfindinganappleineachimageobtainedinuncontrolledlightingconditions.Inordertosegmenttheacquiredimages,twoalgorithmsweredeveloped:edgedetectionbasedandcolor-shapebased.Edgedetection-basedalgorithm:Initialconsiderationsshowedthatthegreengray-scaleimageincludedmostofAdv.J.FoodSci.Technol.,2(6):325-327,2010326thedesiredobjects.Canny(1986)methodwasusedtodeterminetheedgesofapplesingreengray-scaleimage(Fig.1b).Color-shapebasedalgorithm:Thealgorithmwasimplementedbasedonthefollowingsteps:CTheimageswerefirstenhanced.AGaussianlow-passfilterwasusedtoreducethenoiseasmuchaspossible.NoiseportendstounequalcolorintensitydistributionintheoriginalimagesthatformedshadesandshinyregionsintheimagesTheGaussianfilterwasa250250pixelmatrixwithstandarddeviationsof200,whichlimitimagefrequenciestolessthan200MegaHertz(MHz).Filteredimageswerenoise-reducedbyremovinghighfrequencies(morethan200MHz).Filteringtheimagecausedblurringwhichnoisewasreduced(Fig.2a)CFilteredimageswerethenconvertedtobinaryforminordertobeprocessed(Fig.2b)CBinaryimageswereprocessedtoreducetheexistingnoiseafterconvertingimages.Inthisstage,noisewasdefinedastheareasdetectedasfeaturesotherthanapples.Thisstageoftheprojectisshape-basedprocessingofcolor-basedprocessedimages(Fig.2c)CBinary,noise-removedimageswerelabeledtoextracttheapple(Fig.2d)(a)(b)Fig.1:Edgedetectionbasedalgorithm.a)Originalimage,b)Edge-detectedimage(a)(b)(c)(d)Fig.2:Color-shapebasedalgorithm.a)Filteredimage,b)Binaryimage,c)Noise-reducedbinaryimage,d)LabeledimageAdv.J.FoodSci.Technol.,2(6):325-327,2010327RESULTSANDDISCUSSIONThemainideawastodevelopageneralalgorithmundervariousnaturallightingconditions.Thereby,nosupplementallightingsourcewasusedtocontroltheluminance.Sincetheimageswereacquiredunderuncontrollednaturaldaylightconditions,theyincludedtreecanopiesincludingtreebranches,leaves,fruits,sky,etc.Eachobjectoftheimagehasitsownedges,makingimagesetsofedgesofwhichtheappleisjustasubset.So,edgedetectionalgorithmwasnotsuccessful(Fig.1).Color-shapebasedalgorithmdetectedtheimageobjectsintheimagesbetter;however,itwasmorecomplicatedthantheedgedetection.Thestageofcolorprocessingblurredtheimageanditsoutputwasanimagewithlowcontrasthavingdistributedcolors(Fig.2a).Thus,theimagewasnoise-reducedinwhichthenumberofobjectswerelessthanthattheoriginalimage.Convertingtheimagetobinaryformandshape-basedanalysismadethenoiseaslowaspossible(Fig.2b,c).Color-shapebasedalgorithmwasabletodetecttheapplesin25of30images.Inotherwords,theaccuracyofthealgorithmwas83.33%.Figure2showstheprocedureofcolor-shapebasedalgorithm.CONCLUSIONInthisstudy,twoalgorithmsweredevelopedandcomparedtodetectoneappleineachimage.Nolightingcontrolwasexercisedtostandardizeluminanceoftheacquiredimages.However,edgedetectionbasedmethodwasnotsuccessful;color-shapebasedalgorithmwasabletodetectapplesin83.33%ofimages.REFERENCESBulanon,D.M.andT.Kataoka,2010.FruitdetectionsystemandanendeffectorforroboticharvestingofFujiapples.Agric.Eng.Int.CIGRJ.,12(1):203-210.Canny,J.,1986.Acomputationalapproachtoedgedetection.IEEET.PatternAnal.,8(6):679-698.FAO,2009.FoodandAgricultureOrganizationOfficialWe