A SIMULATION BASED STUDY OF A GREENHOUSE S INTELLIGENT FUZZY . - Wireilla
International Journal of Fuzzy Logic Systems (IJFLS) Vol.10, No.1, January 2020A SIMULATION BASED STUDY OF A GREENHOUSESYSTEM WITH INTELLIGENT FUZZY LOGICNiaz Mostakim1, Shuaib Mahmud2 and *Khalid Hossain Jewel31Department of EEE, Atish Dipankar University of Science and Technology,Uttara, Dhaka.2Department of EEE, Jatiya Kabi Kazi Nazrul Islam University, Trishal,Mymensingh, Bangladesh.3Department of EEE, Islamic University, Kushtia, Bangladesh.ABSTRACTGreenhouse (GHS) system is a system, that provides an efficient condition to grow plants. This researchpaper describes designing of a greenhouse system to control climate, soil moisture, lighting using fuzzylogic. The proposed model consists fuzzy logic to control GHS parameter such as temperature, Humidity,light, soil moisture and watering system to the plant. In this proposed system temperature controllingcontroller is used to take current temperature as input by using temperature sensor and its deviation fromuser set data. The temperature is controlled by the speed of fan. This algorithm is same for all otherparameters. In this research the set value of different sensors is selected by the owner of the greenhouseaccording to the basis of growing plant condition. This system will enhance the capability of fuzzy logiccontrol systems in case of process automation and potentiality.Simulation using MATLAB is used toachieve the designed goal.KEYWORDSFuzzy logic, Green house, Sensor, MATLAB Simulink, Machine learning1. INTRODUCTIONA greenhouse is a build-up environment that promotes all the factors of improving the agricultureperformance. It is generally consisting of four parts shown in Figure 1 [1] i.e. Cover of thesurface, soil, plant and internal air. Surface separates the outside environment from insideenvironment. It protects the internal plants from the outsides bad weather and diseases. It can beconsisting of polyethylene of glass. The internal air is the more essential part or components ofthe greenhouse. It is influenced by the external temperature and relative humidity. The soil has tobe considered in this section because of having absorbance and diffusion property of thermal heat.The plants have the important role in heat and water balances of the process. In this paper thethree components i.e. internal air, soil, surface will be discussed. Control system is a device, a setof devices that controls, manages, commands the other devices or system. Industrial controllingsystem are used for the production of industrial equipment or machine. This control system isdesigned, developed and implemented according to the specification of equipment or machine.The performance of the controller depends on the all element included into these systemsdesigned, developed and implemented according to the specification of equipment or machine.The performance of the controller depends on the all element included into this system [2].Computational intelligence system is an intelligent information processing in different sector ofcomputer science. The fuzzy systems are one of the most intelligent system. According totradition logic binary sets have two values true or false, where fuzzy logic variable contains theDOI: 10.5121/ijfls.2020.1010219
International Journal of Fuzzy Logic Systems (IJFLS) Vol.10, No.1, January 2020truth value in the degree of range from 0 to 1. Fuzzy logic has been applied to handle the partialtruth concept. The partial truth ranges from completely truth to completely false. Fuzzy logicemulates the logic as like human thought which is much less inflexible than calculating computergenerally perform. An intelligent control system involves large number of inputs [3]. By usingdifferent types of rules fuzzy logic has been upgrade the computer to think like a human. Usingfuzzy logic algorithm, system has been able to think, enable machine to understand and responsethe human concept i.e. hot, very hot, cold, very cold, normal etc. [4]. Because of being a buildingand occupant thermal interaction for long duration, greenhouse dynamic equation for interior airtemperature has taken in consideration. Fuzzy logic interaction with microcontroller for climatecontrolling has been proposed in [3]. S.D.Dhamakale et al. proposed that for temperature andhumidity control. Fuzzy logic controller for controlling the geothermal of greenhouse has beenproposed by FatenGouadria et al [4]. Temperature humidity, sunlight intensity, wind speed anddirection has included by A. Sriraman et al. Another research work to control temperature,relative humidity, light controlling, Irrigation and nutrient solution control, Carbon Dioxide(CO2) control has been proposed in [5]. Their proposed system mainly optimizes heat and energyof greenhouse and consume water. Climate controlling inside the green house by using fuzzylogic has been proposed in [6]. They implemented the system by using MATLAB for temperatureand humidity controland linguistic variables for sensors and actuators. Low cost fuzzy logiccontrol and web monitoring system has been implemented by C.R Algarin et al. [7]. In thissystem Arduino platform is used to monitor the system with fuzzy logic. Carlos Robles algarin etal. proposed a web page that is designed to monitor the climatic condition of the greenhouse.Fuzzy Logic Controller (FLC) prototype has been proposed by P. Javadikia et.al [3] which issimulated on MATLAB.A. Hilali also proposed a greenhouse climate control system i.e.Temperature and moisture controller system [8]. An approach to control greenhouse based onfuzzy has been proposed by R.Caponetto t et al. [9] Heat loss in the greenhouse system andtemperature controlling by balancing energy of greenhouse has been controlled by intelligentsystem[10] . Fuzzy logic based climate control a wireless data monitoring system has beenimplemented by M. Azaza et. Al [11]. They proposed a system of smart greenhouse system tocontrol both the temperature and humidity of a system to promote a comfortable microclimate forplant growth. They also enhance a platform for data routing and logging to monitor wireless data.Fuzzy logic based climate controlling proposed system has been focused by RafiuddinSyam et al.They monitor the climate, environment of the greenhouse system when watering [12] the plantsand also monitoring and applied a fuzzy logic for the change of humidity and temperature afterwatering. Self-tuning fuzzy logic based PID controller has been implemented by Mahdi Heidariet. al. They proposed a model by using both controller i.e. fuzzy and PID. Here they have showedPID controller [13] and Fuzzy controller to compare the performance of the proposed climatecontrolling system. Satyajit Ramesh Potdar et.al proposed a system to monitor air temperature[14,15,16,17] of greenhouse. Energy balance principle-based greenhouse system has beenproposed for improving the complexity and dynamic of environment.Figure 1. A basic view of Greenhouse system.20
International Journal of Fuzzy Logic Systems (IJFLS) Vol.10, No.1, January 20202. FUZZY CONTROLLER FOR THE PROPOSED GREENHOUSE SYSTEM (GHS)Figure 2 represents the block diagram of the proposed system.In this system five differentsensors, temperature, humidity, rain, moisture and light intensity is used to measure the currentcondition of the plant. The set value is included in the fuzzy set through the membership function.Different fuzzy rules are added to the controller to give the knowledge about the system. Thissystem measures current temperature by using different sensors. All the values are taken by thecontroller. With the help of this measuring value and given set value the controller takes adecision to speedup or speed down or OFF the devices to control the suitable environment insidethe greenhouse. For example, if the temperature is raised from the set-up temperature then thecontroller speeds up the cooler proportion to the raising temperature. If humidity is decreased,then the controller speeds up the vapor supplier to supply more vapor to rise the humidity. Ifhumidity is increased, then the controller speeds up the heater supplier to supply more heat to lessthe humidity. A sample greenhouse controlling system is shown in Figure 3.Figure 2. Block diagram of proposed Greenhouse systemFigure 3. Greenhouse controlling system.21
International Journal of Fuzzy Logic Systems (IJFLS) Vol.10, No.1, January 20203. MATHEMATICAL MODEL OF GREENHOUSE SYSTEMBecause of being a building and occupant thermal interaction for long duration, greenhousedynamic equation for interior air temperature has taken in consideration. For greenhouse thedynamic equation [18] for heat balance isππΆπ π πππ π‘ π π βπππ‘ π ππππ£,ππππ π ππππππ‘ πππππ π βπππ‘ππ π οΏ½ π βπππ‘ Short wave radiation.π ππππ£,ππππ convection and conduction heat transfer rateπ ππππππ‘ Heat loss due to the infiltrationπππππ long wave radiationπ βπππ‘ππ thermal energy provided by the heating systemπ π£πππ‘ππππ‘πππ thermal energy loss from the cooling system.The model operates within from -10 to 45 temperature range. User can set desiredtemperature to control the environment inside the greenhouse. The fuzzy membership function isdesign to smooth controlling of temperature. Here input variable is the temperature sensor thatmeasure current temperature and with the help of set and current temperature, the controllerdecide a value to drive the cooler with desired speed. Temperature of a greenhouse randomlychange due to the disturbance of climate change and can be controlled by maintaining uniformdistribution of climate variable [19]. Heat can be balanced by considering the following equationin above. The short-wave radiation absorbed by greenhouse system can be calculated by thefollowing equation [20].π π βπππ‘ ππ πΌπ ππΌ(2)Where πΌπ is the cover absorptivity of solar radiation, ππ is the cover transmittance, S is the surfacearea (π2 ), and I is the solar radiation (ππ 2).π ππππ£,ππππ ππ(πππ πππ’π‘ )(3)Where, πππ’π‘ represents the outside temperature of the system,πππ represents the measuringtemperature by the temperature sensor and π is the overall heat transfer coefficient through thegreenhouse walls (ππ 2 πΎ 1) and S represents the surface area of the system. The heat loss dueto the infiltration through the greenhouse was calculated using the equation [18] in (4).π ππππππ‘ ππ πΆπ π πππ πππ’π‘3600(4)The greenhouse system absorbed the long wave radiation is calculated by the equation in 5πππππ β0 π(1 ππ )(πππ ππ ππ¦ )(5)Where, ππ ππ¦ is the sky temperature that is suggested by Swinbank [18]. The thermal energyprovided by the heating system is defined asπ βπππ‘ππ πβ π βπ(6)22
International Journal of Fuzzy Logic Systems (IJFLS) Vol.10, No.1, January 2020Where,πβ is the number of heaters, π β is the capacity of the heating system (ππ 2). The thermalenergy loss [18] from the cooling system is represented by the following equation.π π£πππ‘ππππ‘πππ πΆπ π π£(7)Table 1. Sub Systemβs Parameters Value for Temperature Balance of οΏ½οΏ½οΏ½π£Descriptioncover transmittancecover absorptivitysurface areasolar radiationheater outputair densitynumber of air changes per hourinterior air temperatureoutside air temperatureoverall heat transfer coefficientthrough the greenhouse wallsbuilding volumenumber of heaterscapacity of the heating systemventilation rateUnitN/AN/Aπ2ππ 2WπΎππ 3π3 π 1 ππ 2 πΆ 1value0.850.1521.13710052π3N/Aππ 2π3 π 1201104-101.137Figure 4. Sub System for temperature balance of GreenhouseThe relative humidity of the climate is amount of water in the air. For greenhouse to controlhumidity specific parameter are considered as the following equation [21,22,23,24]. π₯ππ£π π‘π ππ΄π(aπΌ πΊ(0))(π₯π π₯π ) πΈ πππ (8)Here, π air densityπ£π Greenhouse volumeA vent areaV wind Speeda, G (0) ventilation parameter23
International Journal of Fuzzy Logic Systems (IJFLS) Vol.10, No.1, January 2020πΌ vent openingπ₯π outside absolute humidityπ₯π inside absolute humidity.E plant transpiration ratefog vapor generation by the fog systemTable 2. Subsystemβs parameters value for humidity balance of Greenhouse.Parameterπ£πAVa, G (0)Ξ±π₯ππ₯πfogEΞ‘DescriptionGreenhouse volumevent areawind Speedventilation parametervent openingoutside absolutehumidityInside absolutehumidity.vapor generation bythe fog systemplant transpirationrateair densityunitπ3π2πππ 1N/AN/AN/Avalue244201.10.170N/A50π3 π 140πΆππππ¦ 10.03πππ 31.137Figure 5. Design of Humidity Control System for GreenhouseLight intensity measurement is the one of the vital parameters in green house system. Theintensity of light can be measured by the light dependent resistor (LDR). The resistance of LDRdepends on the illumination of light (E) and the material used for making LDR sensor. The CDS(Cadmium sulphide) based LDRβs manufacturing value is varied from 0.7 to 0.9. The resistanceof LDR follows the below equation.24
International Journal of Fuzzy Logic Systems (IJFLS) Vol.10, No.1, January 2020R A πΈ π(9)Here R is the resistance, A and a are constant and E is the illumination of light. In this model theparameterβs value used for measuring the resistance and sensor value are focused on the Table 3.Table 3. Sub Systemβs Parameters Value for Light Intensity Measure of Greenhouse.ParameterRaEDescription UnitResistanceohmConstantN/Avalue formanufacturingIllumination (lux)AConstantN/AValue0.9Very Bright SummerDayFull DaylightOvercast Summer DayVery Dark DayTwilightFull Moon10100,000 Lux10,000 Lux1,000 Lux100 Lux10 Lux 1 LuxAccording to the above parameter and its value is used to develop a subsystem which is focusedon the Figure 6.Figure 6. Subsystem of light intensity measurementSensing of rain is very emergent for greenhouse system because of using the rain water for plantto increase the moisture of soil according to required value. For this reason, rain sensor is used tomeasure rainfall. A rain sensor plays a vital rule to detect the rain. The rain sensors workingprocedure is same as comparator circuit. The designing parameter of rain sensor acting process indescribe in below Table 4. The equation of finding degree of rainfall is described in below.π π πππ’π‘ π ππ π 2 -πππ12(10)Table 4. Sub Systemβs parameters value for rainfall value measure of Greenhouse.25
International Journal of Fuzzy Logic Systems (IJFLS) Vol.10, No.1, January 2020Parameter οΏ½οΏ½π 1Description of parametersUnitValueResult of rain sensorSensing valueVariable resistorSupply voltageFixed ResistorVoltvoltKilo ohmvoltKilo ohm210101Figure 7. Subsystem for rain intensity measurementSensing of amount of water present in soil is very important for plant to rise inside the greenhousesystem. For this purpose, Expert system moisture sensor is needed to monitor the water amountneeded for plant. The Soil moisture sensor follows the below equation.ππ1 2 π₯π ππ1 2 π₯π ππ1 2 π₯π€ ππ€1 2(11)Dielectric sensor only senses the bulk dielectric permeability of soil not water contents.The relationship between bulk dielectric constant and soil water content is used to measureaccuracy.Where, Ξ΅ is used to represent the relative dielectric permittivity, volume fraction is represented byx and the subscripts b, a, m, and wrepresent the properties of material i.e. bulk, air, mineral, andwater respectively. The permittivity of air is 1. The permittivity of soil minerals can range from 3to 16 but a value of 4 is often used. We can substitute for π₯π the expression 1 βπ₯π€ βπ₯π and for π₯ππthe ratio of bulk to particle density of the soil, πππ Table 5. Subsystemβs parameters value for soil moisture Measure of Greenhouse.QuantityBulk PermittivityWater PermittivityMineral PermittivityBulk DensityParticle οΏ½οΏ½οΏ½Nominal Value108041.32.6526
International Journal of Fuzzy Logic Systems (IJFLS) Vol.10, No.1, January 2020Figure 8. Subsystem for Soil moisture measurement4. MODELLING OF FUZZY CONTROLLERA proposed GHS with fuzzy controller has showed in Figure 2. This system is designed to controltemperature, humidity, light, moisture and water pump of a greenhouse. The algorithm isdesigned based on fuzzy logic to smooth controlling every parameter of greenhouse. Thisalgorithm is used to design the fuzzifier,defuzzifier according to the control strategy of theprocessing plant to ensure quality.4.1. Temperature controllingThe greenhouse parameter temperature is controlled by using fuzzy logic. This designing systemis controlled at -10 to 45 degree centigrade. This temperature is divided into five sections anddefined as membership function. This designing membership function is showed in Table 6.Table 6. Membership function of current Temperature.Membership functionRange (Degree Centigrade)Very cold (VC)Cold(C)Normal (NOR)HOTVery Hot (VHOT)-10 to 2.5-1 to 1512 to 2725 to 3632 to 45Temperature is mainly controlled by the heater. When the heater in ON or OFF state dependingon the current temperature of the room, Fuzzy controller helps to speed up the heating supplywhere the traditional logic has only OFF and ON state. The output variable heater has 3membership function OFF, LOW and HIGH.27
International Journal of Fuzzy Logic Systems (IJFLS) Vol.10, No.1, January 2020Table 7. Membership function of Heater.Membership Function of HeaterOFFLOWHIGHPoint of value0,0,2.50,2.5,52.5,5,5When the temperature will rise from the set temperature then the cooler will automatically ONand will speed up automatically. For cooler there are 3 membership function, OFF, LOW andHIGH. The fuzzy controller takes a decision what type of operation will happen to control thetemperature.4.2. Controlling HumidityRelative Humidity is measured by the percentage of water vapour in air and in relation to theamount of holding at a given temperature. The Humidity that gives comfortable atmospheredepends on temperatures, indicated in below. This designing five membership functions from therange of 0 to 100 percentage of humidity that is showed in Table 9.Table 8. Membership function of HumidityMembership functionVery Low(VL)Low(L)Normal(NOR)HIGH(H)Very High(VHIGH)Range (% )0 to 2010 to 4030 to 5550 to 7060 to 100When the humidity will rise from the set humidity then the vapor supplier will automatically ONand will speed up automatically. For vapor supplier there are 3 membership function, OFF, LOW,HIGH. The fuzzy controller takes a decision what type of operation will happened to control thehumidity.Table 9. Membership function of vapor.Membership Function of VaporOFFLOWHIGHPoint of value0,0,2.50,2.5,52.5,5,54.3. Light controllingThe light intensity of the greenhouse has been controlled using fuzzy controller. The intensitymeasured by light sensor LDR and the value range from 0 to 10. The intensity level will haveincreased then the output value will have increased. To control proper intensity three membershipfunction LOWLIGHT, NORLIGHT and HIGHLIGHT have declared. And the range of eachmembership function has given on the Table 10.Table 10. Membership function of Light Intensity.Membership function of light sensorRange (Light Intensity)LOWLIGHTNORLIGHTHIGHLIGHT0 to 3.52.8 to 7.246.3 to 1028
International Journal of Fuzzy Logic Systems (IJFLS) Vol.10, No.1, January 2020The light controlling device Lamp will be derived by the controller and lampβs intensity willincrease and decreased by the value of three membership function.4.4. Moisture controllingThe moisture of the soil is most important for plants. In the greenhouse system the moisture of thesoil is controlled by using fuzzy logic. The three-membership function have declared to thecontroller in the range between 0 and 50 and the declared membership function is DRY,NORMAL, WET. The value is selected as the Table 11 in below.Table 11. Membership function of Moisture.Membership function of moistureRange (Moisture value)DRYNORMALWET0 to 2012 to 3528 to 50To control the moisture, the water pump motor is used. The motor serves water at three conditioni.e. OFF, LOW and HIGH. This water pump will ON or OFF depend on the rain sensor. Thedesign value of input moisture function has showed in Table 12.Table 12. Membership function of Water pump.Membership Function of water pumpPoint of valueOFFLOWHIGH0,0,2.50,2.5,52.5,5,5The rain sensor detects the current rain status and fuzzy control system select three membershipfunction NRAIN, LIGHTRAIN, HEAVY RAIN. If the rain present, then the rain water will besupplied to the soil and the main water pump will not ON. The rainfall-based input membershipfunction has been given out at Table 13.Table 13. Membership function of Rain Sensor.Membership function of Rain sensorRange (Rain intensity)NORAINNORMALRAINHEAVYRAIN0 to 3.52.8 to 7.35.8 to 10Rain water will be supplied to the soil if the soil moisture status is dry and the rain water ispresent. For rain water supply two-member function OFF and OPEN have selected. The roofmotor will be ON or OFF according to the designing rules in fuzzy logic. The output membershipfunction for roof motor has been focused at Table 14.29
International Journal of Fuzzy Logic Systems (IJFLS) Vol.10, No.1, January 2020Table 14. MembePrship function of Roof Motor.Membership function of Roof motorRange (Roof motor supplyvoltage)OFFNORMALHIGH0 to 21.5 to 3.53 to 54.5. Rules of greenhouse systemThe rules set up for fuzzy logic-based greenhouse parameter controlling system have shown inTable15. This table contains the fuzzy possible logic for this considering parameter of greenhousecontrolling system.Figure 9. Model of greenhouse system using Fuzzy logic.Figure 10. Matlab Rules View.30
International Journal of Fuzzy Logic Systems (IJFLS) Vol.10, No.1, January 2020Table 14. Rules view of fuzzy logic.5. RESULT & DISCUSSIONThis proposed system has been tested in Matlab and for different type of sensing value we get adifferent output response of controlling device. For a current parameter value the response ofdevice are showed in the Figure 10. In Matlab the simulation run for 10 secs and the controllingoutput is shown in different scope. In this simulation system the set temperature value was 24degrees centigrade. The temperature gains and losses by this system give a temperature value ofTin. The error signal between system and set temperature is backed to the input of the system.Then the systemβs fuzzy controller decides to change the output device such as cooler which isshown in Figure 11. The curve shows that the cooler is stable after the stability of set temperature.In Figure 12, time vs sensor output for heater control is showed. The heater controlling outputwill be stable after certain period of time. Initially the systemβs temperature is not matched as theset's temperature so the unstable signal of heater control is showed in Figure 12.Figure 11. Time vs sensor output for Cooler control31
International Journal of Fuzzy Logic Systems (IJFLS) Vol.10, No.1, January 2020Figure 12. Time vs sensor output for Heater control.Figure 13. Time vs sensor output for lamp control.In Figure 13 when the light intensity changes the output device corresponding to this in-put willbe changed. In this fig the output data is a straight line so the signal is stable at the start of time.In Figure 14 when the roof motor controlling output is showed, it depends on the twoparameters, i.e. Rainfall and moisture sensor value. If these two parameters are true, then theroof motor will On and the sensor output value will be changed.32
International Journal of Fuzzy Logic Systems (IJFLS) Vol.10, No.1, January 2020Figure 14. Time vs sensor output for lamp control.when the humidity will be less than this controlling output is delivered at maximum value whichis showed in Figure 15. This curve is also a stable outputFigure 15. Time vs sensor output for vapor control.Figure 16. Time vs sensor output for water pump control.33
International Journal of Fuzzy Logic Systems (IJFLS) Vol.10, No.1, January 2020In Figure 16 the sensor output for water pump control is focused. The water pumps signaldepends on the moisture sensor and rainfall sensors.This signal start as a constant value from thestart of simulation. In the proposed model of Different parameters of greenhouse system, thebalancing output temperature has been showed in Figure 17Figure 17. Sub system output of Temperature modelβs output.Figure 18. Sub system output of Humidity modelβs output.Figure 19. Sub system output of Light intensity modelβs output.34
International Journal of Fuzzy Logic Systems (IJFLS) Vol.10, No.1, January 2020Figure 20. Sub system output of Rain sensing modelβs output.Figure 21. Sub system output of Moisture modelβs output.6. CONCLUSIONSThis designing method makes the system efficient and better controlling. This analytical valueclearly maps out the functioning of fuzzy logic in dealing with the problem of different smoothcontrolling in difficult situation. In this greenhouse system fuzzy logic helped to solve thecomplex problem without interaction physical variables. Intuitional knowledge about input andoutput parameters was enough to design an optimally performance of this system. This proposedsystem is being carried out in pro-cessing plant and in future it will help to design the advancedcontrolling system for various application in environment monitoring and management system.This system is mainly proposed for monitoring and maintaining the environment of greenhouse sothat an eco-friendly environment for producing any kind of plant. In future more advancedcontrolling system can be introduced i.e. measuring PH for maintaining proper PH level of waterto supply suitable water for plant.35
International Journal of Fuzzy Logic Systems (IJFLS) Vol.10, No.1, January 2020REFERENCES[1]M. A. Ali.Ben.R, Aridhi.E, βDynamic model of an agricultural greenhouse using Matlab-Simulinkenvironment,β in 16th international conference on Sciences and Techniques of Automatic controlcomputer engineering - STA, 2015, pp. 21β23[2]D. Das.Kumar.T, βDesign of A Room Temperature and Humidity Controller Using Fuzzy Logic,βinAmerican Journal of Engineering Research (AJER), Volume-02,Issue-11, pp. 86β 97[3]O. A. F. Javadikia.P, Tabatabaeefar. A, βEvaluation of Intelligent Greenhouse Climate ControlSystem, Based Fuzzy Logic in Relation to Conventional Systems,β in International Conference onArtificial Intelligence and Computational Intelligence. 2009[4]S. Gouadria.F, βFuzzy logic controller devoted to a geothermal greenhouse,β in 3rd internationalconference on Automation, control, Engineering and computer science, ACECS, 2016.[5]M. Sriraman.A, βClimate Control inside a Greenhouse: An Intelligence System Approach UsingFuzzy Logic Programming,β in Journal of Environmental Informatics, 2007, pp. 14β20[6]Z. Oliver L. Iliev, Sazdov.P, βA Fuzzy Logic Based Controller for Integrated Control of ProtectedCultivation,β in FactaUniversitatis, Series: Automatic Control and Robotics, 2012, pp. 119β128[7]L. A. AlgarΓ n R.C. Iliev, Cabarcas.C.J, βLow-Cost Fuzzy Logic Control for GreenhouseEnvironments with Web Monitoring ,β in Basel, Switzerland.[8]H. R. Hilali.A, Alami, βControl Based On the Temperature and Moisture, Using the Fuzzy Logic, βinInt. Journal of Engineering Research and Application.[9]N. O. Caponetto. R, ortuna.L, βA Fuzzy Approach to Greenhouse Climate Control,β in AmericanControl Conference., 1998.[10] S. Revathi.S, βFuzzy Based Temperature Control of Greenhouse,β in Science Direct,IFACPapersOnLine., 2016, pp. 549-554.[11] F. M. Azaza.M, anougast.c, βSmart greenhouse fuzzy logic based control system enhanced withwireless data monitoring,β in ISA Transaction.[12] P. W. H. Syam.R and J., βControlling Smart Green House Using Fuzzy Logic Method,β inInternational Journal on Smart Material and Mechatronics., 2015.[13] K. Heidari.M, βClimate Control of An Agricultural Greenhouse by Using Fuzzy Logic SelfTuningPID Approach,β in Proceedings of the 23rd International Conference on Automation Computing.,2017[14] M. Potdar .R.S, Patil.B.C, βGreenhouse Air-Temperature Modelling and Fuzzy Logic Control,β inInternational Journal of Electronics Engineering Research., 2017, pp. 727β734[15] B. J.-F. Lafont.F, βOptimized fuzzy control of a greenhouse,β in Fuzzy Sets and Systems 128, 2002,pp. 47-59[16] S.-L. F. Bouadila.S, Kooli.S, βImprovement of the greenhouse climate using a solar air heater withlatent storage energy,β 2014, pp. 663β672[17] F. A. A. Khalid A. Joudi, βA dynamic model and an experimental study for the internal air and soiltemperatures in an innovative greenhouse,β in Energy Conversion and Management 91, 2015, pp. 768236
International Journal of Fuzzy Logic Systems (IJFLS) Vol.10, No.1, January 2020[18] A. M. ALI.B.Rim, ARIDHI.E, βFuzzy Logic Controller of temperature and humidity inside anagricultural greenhouse,β in 7th International Renewable Energy Congress (IREC), 2016, pp[19] O. X. Caponetto.R, Nunnari.G, βSoft Computing for Greenhouse Climate Control,β in IEEETRANSACTIONS ON FUZZY SYSTEMS, 2000[20] A. A. M. M. ALI.B.R, F, βFuzzy Logic Controller of temperature and humidity inside an agriculturalgreenhouse,β in 7th International Renewable Energy Congress (IREC), 2016[21] B. X. S. J. Senent.S.J, Martfnez.A.M, βMIMO Predictive Control of Temperature and HumidityInside a Greenhouse Using Simulated Annealing (SA) as Optimizerof a Multicriteria Index,βinInternational Conference on Industrial, Engineering and Other Applications of AppliedIntelligentSystem, 2005, pp. 271β279[22] P. Barsoum.N, βGSM Greenhouse Monitoring and Control of Temperature and Soil Moisture,β inEuropean International Journal of Science and Technology, 2015[23] R. Kavitha.A, βSolar Based Greenhouse Automation System for Concrete Roof,βin InternationalJournal of Mathematical Sciences and Engineering (IJMSE), 2017[24] M. R. K. P. Shirsath.O.D, Kamble.P, βIOT Based Smart Greenhouse Automation Using Arduino,β inInternational Journal of Innovative Research in Computer Science Technology (IJIRCST), 2017, pp[25] S.
International Journal of Fuzzy Logic Systems (IJFLS) Vol.10, No.1, January 2020 20 truth value in the degree of range from 0 to 1. Fuzzy logic has been applied to handle the partial truth concept. The partial truth ranges from completely truth to completely false. Fuzzy logic
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