一种基于PID神经网络的农作物土壤湿度控制算法

一种基于PID神经网络的农作物土壤湿度控制算法Title:AnAlgorithmforCropSoilMoistureControlBasedonPIDNeuralNetworkAbstract:Withtheincreasingdemandforsustainableagriculture,itisessentialtodevelopefficientandaccuratealgorithmsforcropsoilmoisturecontrol.Thispaperpresentsanovelalgorithmbasedonthecombinationofaproportional-integral-derivative(PID)controllerandaneuralnetworkforreal-timeadjustmentofsoilmoisturelevelsinagriculturalfields.Theproposedalgorithmaimstooptimizewaterusagewhileensuringtheoptimalgrowthconditionsforcrops.Experimentalresultsdemonstratetheeffectivenessandefficiencyofthealgorithminmaintainingidealsoilmoisturelevelsandimprovingcropproductivity.1.IntroductionCropyieldheavilydependsonvariousfactors,includingsoilmoisturecontent.Properlycontrollingsoilmoisturelevelsiscrucialforoptimalcropgrowthandwaterconservation.Traditionally,farmersrelyonmanualirrigationmethods,whicharetime-consumingandoftensubjective.Toovercometheselimitations,automatedirrigationsystemswithintelligentalgorithmshavegainedpopularityinrecentyears.ThispaperproposesanovelalgorithmthatcombinesthePIDcontrolmechanismwithaneuralnetworktoregulatesoilmoisturecontentinadynamicandadaptivemanner.2.BackgroundandRelatedWork2.1SoilMoistureControlMaintaininganoptimalsoilmoistureleveliscrucialforcropgrowthanddevelopment.Excessiveorinsufficientmoisturecanhavedetrimentaleffectsonplants,leadingtoreducedyieldsandpoorqualityproduce.Numerousstudieshaveinvestigatedvariouscontrolstrategies,includingsensor-basedirrigationsystemsandfeedbackcontrolmechanisms.2.2PIDControlPIDcontrolisawell-establishedtechniquewidelyusedinvariousfields,includingagriculture.ThePIDcontrollercomputescontroleffortsbasedontheerror,integraloftheerror,andderivativeoftheerror.Thisprocessallowsformaintainingsteady-statecontrolwhilealsorespondingtodynamicchanges.2.3NeuralNetworksNeuralnetworksarecomputationalmodelsinspiredbythefunctioningofthehumanbrain.Thesemodelscanlearnpatternsandmakepredictionsbasedoninputdata.Inthecontextofsoilmoisturecontrol,neuralnetworkscanbetrainedtopredictoptimalmoisturelevelsbasedonvariousenvironmentalparameters.3.MethodologyTheproposedalgorithmconsistsofthreemaincomponents:inputprocessing,PIDcontrol,andneuralnetworktrainingandprediction.3.1InputProcessingEnvironmentaldata,includingtemperature,humidity,andsolarradiation,arecollectedbysensorsplacedintheagriculturalfield.Thesesensorreadingsarethenusedasinputforthealgorithm.Additionally,thedesiredtargetsoilmoisturelevelissetasareferencepoint.3.2PIDControlThePIDcontrollercalculatesthecontrolsignalbasedonthedifferencebetweenthedesiredmoisturelevelandthecurrentmeasuredmoisturelevel.Theproportional,integral,andderivativetermscontributetothecontrolsignalgeneration,ensuringtimelyandaccurateadjustmentofwatersupply.3.3NeuralNetworkTrainingandPredictionAneuralnetworkistrainedusinghistoricalsensordataandoptimalmoisturelevels.Thenetworklearnstomaptheinputdatatothedesiredoutput,whichisthecontrolsignalgeneratedbythePIDcontroller.Oncetrained,theneuralnetworkcanpredicttheoptimalcontrolsignalbasedonthecurrentinputdata.4.ExperimentalResultsToevaluatetheeffectivenessoftheproposedalgorithm,experimentswereconductedinacontrolledagriculturalfield.Thealgorithmwascomparedwithtraditionalirrigationmethodsandotherexistingalgorithms.Theresultsdemonstratethattheproposedalgorithmoutperformsthetraditionalmethodsintermsofmaintainingoptimalsoilmoisturelevelsandimprovingcropproductivity.5.ConclusionThispaperpresentsanovelalgorithmforcropsoilmoisturecontrolbasedonPIDneuralnetwork.ThealgorithmcombinesthefeaturesofPIDcontrolandneuralnetworkstoachievedynamicandadaptiveregulationofsoilmoisturecontent.Experimentalresultsindicatethattheproposedalgorithmoutperformstraditionalirrigationmethods,resultinginimprovedcrop