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I.J.Image,GraphicsandSignalProcessing,2014,5,53-63PublishedOnlineApril2014inMECS()DOI:10.5815/ijigsp.2014.05.07Copyright©2014MECSI.J.Image,GraphicsandSignalProcessing,2014,5,53-63ArtificialNeuralNetworksinFruits:AComprehensiveReviewSumitGoyalMember,IDA,NewDelhi,IndiaE-mail:thesumitgoyal@gmail.comAbstract—Thisreviewdiscussestheapplicationofartificialneuralnetworks(ANN)modelinginfruits.ItcoversallfruitsinwhichANNmodelinghasbeenapplied.ANNisquiteanewandeasycomputationalmodelingapproachusedforprediction,whichhasbecomepopularandacceptedbyfoodindustry,researchers,scientistsandstudents.ANNshavebeenappliedinalmosteveryfieldofscienceandtechnology,viz.,speechsynthesis&recognition,patternclassification,adaptiveinterfacesbetweenhumans&complexphysicalsystems,clustering,functionapproximation,imagedatacompression,non-linearsystemmodeling,associativememory,combinatorialoptimization,controlandseveralothers,astheyhaveprovedvaluabletoolsforobtainingtherequiredoutput.ANNprovidesanexcitingalternativemethodforsolvingavarietyofproblemsindifferentareasofscienceandengineering.TheaimofthiscommunicationistodiscovertherecentadvancesofANNtechnologyimplementedinfruits,anddiscussthecriticalrolethatANNplaysinpredictivemodelling.IndexTerms—Artificialneuralnetworks(ANN),machinelearning,backpropagation,fruits,neurocomputing,softcomputingI.INTRODUCTIONThecomprehensiveuseofartificialneuralnetworks(ANN)inavarietyofapplicationsmakesitanessentialtoolinthedevelopmentofproductsthathaveimplicationsforthehumanworld.TherolemodelforANNisthehumanmind.ANNisacollectionofmethodologiesthatintendtoutilizetheenduranceforambiguityanduncertaintytoachievecompleteinformationandprovidelowcostsolutions.ANNisplayinganincreasinglysignificantroleinmanyapplicationareasofscience,engineeringandtechnology.ANNcanberegardedasanextensionofmanyclassificationtechniques,whichhavebeendevelopedoverseveraldecades.Thesenetworksareinspiredbytheconceptofthebiologicalnervoussystem,andhaveprovedtoberobustindealingwiththeambiguousdataandthekindofproblemsthatrequiretheinterpolationoflargeamountsofdata.Insteadofsequentiallyperformingaprogramofinstructions,neuralnetworksexploremanyhypothesessimultaneouslyusingmassiveparallelism.Neuralnetworkshavethepotentialforsolvingproblemsinwhichsomeinputsandcorrespondingoutputvaluesareknown,buttherelationshipbetweentheinputsandoutputsisnotwellunderstoodorisdifficulttotranslateintoamathematicalfunction.Theseconditionsarecommonlyfoundintasksinvolvinggrading,sortingandidentifyingagriculturalproducts[1-6].Inpresentera,theconsumersareextremelyconsciousaboutqualityofthefoodstheybuy.Regulatoryagenciesarealsoveryvigilantaboutqualityandsafetyissuesandinsistonthemanufacturersadheringtothelabelclaimsaboutqualityandshelflife.Suchdiscerningconsumers,therefore,poseafargreaterchallengeinproductdevelopmentandmarketing.Tilldatenosuchreviewispresentintheacademicliterature,whichdescribestheuseofANNinfruits.Thiscommunicationreportsanddiscussesawidevarietyoffruits,viz.,apple,applejuice,avocado,apricot,banana,blackberry,blackcurrant,blueberries,cherries,datefruit,fruitjuices,eggplant,gooseberry,grape,grapejuice,guava,jackfruit,mandarin,mango,mangosteen,mosambi(sweetlime)juice,honeydewmelon,orange,orangejuice,pears,pineapple,pomegranate,pomelo,satsuma,starfruits,strawberry,tomatoes,tangerineandwatermelon.Therefore,thisstudywouldbeextremelybeneficialtothefruitcultivators,agriculturalscientists,researchers,students,farmers,fruitprocessingindustry,consumersandregulatoryagencies,asitprovidesalltherelevantpublishedliteratureatoneplace.II.ANNINFRUITSANNhasbeenappliedinalmosteveryaspectofagriculturalscienceoverpasttwodecades,yetmostapplicationsareinthedevelopmentstage.ANNsareusefultoolsforfoodsafetyandqualityanalyses,whichincludemodelingofmicrobialgrowth,andfromthispredictingfoodsafety;interpretingspectroscopicdata,predictingphysical,chemical,functionalandsensorypropertiesofvariousagriculturalproductsduringprocessing,storageanddistribution.ANNsholdagreatdealofpromiseformodelingcomplextasksinprocesscontrolandsimulationandinapplicationsofmachineperceptionincludingmachinevisionandelectronicnoseforfoodsafetyandqualitycontrol[7].TheapplicationofANNforpredictingtheshelflifeoffoodproductsin54ArtificialNeuralNetworksinFruits:AComprehensiveReviewCopyright©2014MECSI.J.Image,GraphicsandSignalProcessing,2014,5,53-63foodindustryisquiteanewandeffectiveapproach.TheANNtechniquetodairyproductsisappliedmainlybecausetheshelflifeevaluationinthelaboratoryisverycumbersome,expensive,andtime-consuming,whiletheANNprocedureissensitive,reliable,fast,simpleandlow-costmethodformonitoringtheauthenticityoftheproducts,whichprovideconsumerswithasaferfoodsupply[8].ThepublishedliteratureforvariousfruitsusingANNmodelingispresentedbelow:A.AppleANNmodelingandseveralmathematicalmodelswereappliedtopredictthemoistureratioinanappledryingprocessbyinvestigators.Fourdryingmathematicalmodelswerefittedtothedataobtainedfromeightdryingrunsandthemostaccuratemodelwasselectedbyresearchers.TwosetsofANNmodeli