A newcontrolled seed metering unit was designed and mounted on a common grain drill for direct seeding of wheat(DSW).It comprised the following main parts:(a)a variable-rate controlled direct current motor(DCM)as seed...A newcontrolled seed metering unit was designed and mounted on a common grain drill for direct seeding of wheat(DSW).It comprised the following main parts:(a)a variable-rate controlled direct current motor(DCM)as seed metering shaft driver,(b)two digital encoders for sensing the rotational speed of supplemental ground wheel(SGW)and seed metering shaft and(c)a control box to handle and process the data of the unit.According to the considered closed-loop control system,the designed control box regularly checked the revolution per minute(RPM)of seed metering shaft,as operation feedback,using its digital encoder output.The seeding ratewas determined based on the calculated error signal and output signal of the digital encoder of the SGW.A field with four different levels of wheat stubble coverage(10%,30%,40%and 50%)was selected for evaluation of the fabricated seed metering unit(FSMU).The dynamic tests were conducted to compare the performance of installed FSMU on the grain drill and equipped grain drill with common seed metering unit(CSMU)at three forward speeds of 4,6 and 8(Km/h)for DSW.Results of the FSMU assessment demonstrated that an increase in forwardspeed of grain drill(FSGD)and stubble coverage did not significantly affect the seeding rate in the grain drill forDSW.Using theFSMU reduced the coefficient of variation(CV)by approximately 50%.Consequently,applying the FSMU on the common grain drill led to a desirable seeding rate at different forward speeds of the grain drill and stubble existence.展开更多
Tractor fuel efficiency parameters(TFEPs)(fuel consumption per working hour(FCWH),fuel consumption per tilled area(FCTA)and specific volumetric fuel consumption(SVFC))were intelligently simulated.A neurocomputing base...Tractor fuel efficiency parameters(TFEPs)(fuel consumption per working hour(FCWH),fuel consumption per tilled area(FCTA)and specific volumetric fuel consumption(SVFC))were intelligently simulated.A neurocomputing based simulation strategy(adaptive neurofuzzy inference system(ANFIS))was used to simulate the TFEPs.A comparison was also made between results of the best ANFIS environment and those of another neurocomputing based simulation strategy,artificial neural network(ANN).Field experiments were conducted at plowing depths of 10,20 and 30(cm)and forward speeds of 2,4 and 6(km/h)using a disk plow implement.Statistical descriptor parameters applied to evaluate simulation environments indicated that the best simulation environment of both ANFIS and ANN were able to perfectly predict the TFEPs.However,the best comprehensive ANN simulation environment with a simple architecture of 2-6-3 was easier to use than three individual ANFIS simulation environments.The ANN results revealed that simultaneous increase of forward speed from 2 to 6(km/h)and plowing depth from 10 to 30(cm)led to nonlinear increment of the FCWH from 5.29 to 14.89(L/h)and nonlinear decrement of the SVFC from 2.95 to 0.67(L/h kW).Meanwhile,forward speed increment along with plowing depth decrement resulted in nonlinear decrement of the FCTA from 28.13 to 12.24(L/ha).Interaction of forward speed and plowing depth on the FCWH and SVFC was congruent,while it was incongruent for the FCTA.It is suggested to employ the ANN environment in developing future fuel planning schemes of tractor during tillage operations.展开更多
This paper deals with implementation of intelligent simulation configurations for prediction of tractor wheel slip in tillage operations.The effects of numeral variables of forward speed(2,4,and 6 km/h)and plowing dep...This paper deals with implementation of intelligent simulation configurations for prediction of tractor wheel slip in tillage operations.The effects of numeral variables of forward speed(2,4,and 6 km/h)and plowing depth(10,20,and 30 cm),and nominal variable of tractor driving mode(two-wheel drive(2WD)and four-wheel drive(4WD))on tractor rear wheel slip were intelligently simulated utilizing data mining methodologies of artificial neural network(ANN)and adaptive neuro-fuzzy inference system(ANFIS).Neuro-fuzzy potential of the ANFIS simulation framework against neural ability of the ANN simulation framework was apprised.Results confirmed higher efficiency of the best configuration of the ANFIS simulation framework with satisfactory statistical performance criteria of coefficient of determination(0.981),root mean square error(1.124%),mean absolute percentage error(1.515%),and mean of absolute values of prediction residual errors(1.135%)than that of the ANN simulation framework.Physical perception obtained from the ANFIS simulation results demonstrated that the wheel slip increased nonlinearly with increment of forward speed and plowing depth,while it decreased as tractor driving mode changed from the 2WD to 4WD.Therefore,the best configuration of the ANFIS based intelligent simulation framework implemented in this study can be used for further relevant studies of tractor rear wheel slip as a reference.展开更多
This study is dedicated to examine predictive ability of neural computing environments,based on artificial neural network(ANN)and adaptive neuro-fuzzy inference system(ANFIS)strategies,for integrated simulation of ult...This study is dedicated to examine predictive ability of neural computing environments,based on artificial neural network(ANN)and adaptive neuro-fuzzy inference system(ANFIS)strategies,for integrated simulation of ultrasound-assisted hydration kinetics of wheat kernel.Hydration process was accomplished at five hydration temperatures of 30,40,50,60 and 70C in ultrasonication conditions named control(without ultrasound treatment),US1(25 kHz,360 W)and US2(40 kHz,480 W).The hydration temperature,ultrasonication condition,and hydration time were used as input variables and moisture content was taken as output variable in the neural computing simulation environments.On account of statistical performance criteria,the distinguished ANFIS simulation environment with coefficient of determination of 0.991,root mean square error of 2.478%d.b.,mean relative deviation modulus of 4.301%and average of absolute values of simulation residual errors of 1.863%d.b.was better performed than the distinguished ANN simulation environment.The ANFIS simulation results showed that individual or simultaneous increment of hydration temperature and hydration time caused nonlinear increment of moisture content at any given ultrasonication condition.Moreover,physical perception obtained from the integrated ANFIS simulation results indicated congruency effect(sponge and acoustic cavitation)of cutting-edge ultrasound technology on water absorption.The ANFIS simulation results improved the state of art in domain of studying ultrasoundassisted hydration process of wheat.Therefore,the distinguished ANFIS simulation environment is suggested to be served as an effective step towards management of ultrasound-assisted hydration process of wheat in seed priming,flour milling(tempering),making dough,and wet storage processes.展开更多
Energy indices(energy requirement for tillage implement(ERTI)and tractor overall energy efficiency(TOEE))of tractor-implement during tillage operations were aimed to be investigated in this study.To generate a new com...Energy indices(energy requirement for tillage implement(ERTI)and tractor overall energy efficiency(TOEE))of tractor-implement during tillage operations were aimed to be investigated in this study.To generate a new comprehensive model,the effects of forward speed at three levels(2,4 and 6 km/h)and plowing depth at three levels(10,20 and 30 cm)on energy indices were experimentally evaluated.Two soft computing techniques,artificial neural network(ANN)and adaptive neuro-fuzzy inference system(ANFIS),were employed to prognosticate energy indices.Comparison between the best developed structure of each soft computing technique demonstrated that one comprehensive ANN model was preferred than two individual ANFIS models.According to the ANN prognostication results,simultaneous increase of forward speed from 2 to 6 km/h along with plowing depth increment from 10 to 30 cm led to nonlinear increment of the ERTI and TOEE from 33.87 to 122.66 MJ/ha and 4.65 to 17.85%,respectively.Moreover,interaction of forward speed and plowing depth on energy indices was congruent.Development of comprehensive ANN model now makes it possible to answer fundamental questions in domain of the effect of plowing depth and forward speed on energy indices of tractor-implement that were previously intractable.Hence,to properly manage energy indices and reduce energy dissipation of tractor-implement,application of the new developed ANN model is strongly recommended.展开更多
Friction coefficients(static friction coefficient(SFC)and dynamic friction coefficient(DFC))of pomegranate seed on different structural surfaces(glass,aluminum,plywood,galvanized steel and rubber)as affected by moistu...Friction coefficients(static friction coefficient(SFC)and dynamic friction coefficient(DFC))of pomegranate seed on different structural surfaces(glass,aluminum,plywood,galvanized steel and rubber)as affected by moisture content(4-21.9%(d.b.))and sliding velocity(1.4-16(cm/s))were investigated.Analysis of variance(ANOVA)was performed to determine the effect of main treatments and their interactions on SFC and DFC.Significance of single or multiple effect of the main treatments with five levels was assessed using Duncan’s multiple range test(DMRT).To predict SFC and DFC,multiple linear regression(MLR)modeling technique was applied for each type of structural surface.The goodness of fit of each MLR model was evaluated using statistical parameters:coefficient of determination,root mean square error and mean relative deviation modulus.Results showed that the minimum and maximum SFC or DFC were in minimum and maximum moisture content on glass and rubber surface,respectively.ANOVA table indicated the significant effect of main treatments and their interactions on SFC and DFC at significance level of 1%(P<0.01).According to DMRT results,SFC linearly increased as moisture content increased and DFC increased also linearly as individual or simultaneous increment of moisture content and sliding velocity occurred,for all experimental conditions.According to the obtained statistical parameters,both SFC and DFC were properly predicted by means of MLR modeling technique.展开更多
Tendency towards computer simulations linked to agricultural machinery has enormously increased in recent years.In this regard,the principal contribution of current research was to develop soft computing simulation wo...Tendency towards computer simulations linked to agricultural machinery has enormously increased in recent years.In this regard,the principal contribution of current research was to develop soft computing simulation workplaces for performance prognostication of tractor-implement system in plowing process.Two neurofuzzy strategies based on multiple adaptive neuro-fuzzy inference systems(MANFIS)scenario and the MANFIS coupled with multiple nonlinear equations(MNE)scenariowere executed in theworkplace.Additionally,neural strategy based on artificial neural network(ANN)scenario was also fulfilled in the workplace.Operational variables of plowing depth(10–30 cm),forward speed(2–6km/h),and tillage implement type(moldboard,disk,and chisel plow)were considered as theworkplace inputs and ten performance parameters were taken as the workplace outputs.According to the obtained prognostication accuracy,simulation time,and user-friendly configuration of three scenarios(ANN,MANFIS,andMANFIS+MNE),the MANFIS+MNE was recognized as the prominent simulation scenario.According to the MANFIS+MNE workplace results,for each tillage implement,the compound effect of plowing depth and forward speed on some performance parameters(required draft force of implement,tractor rear wheel slip,fuel consumption per working hour,specific volumetric fuel consumption,tractor drawbar power,energy requirement for tillage implement,overall energy efficiency,and tractor tractive efficiency)was nonlinearly synergetic.However,it was nonlinearly antagonism in case of specific draft force and fuel consumption per tilled area.The MANFIS+MNE workplace simulation results provide opportunity for technical farmer associations involved in the decision-making of agricultural machinerymanagement in order to gain exhaustive fundamental insights into the compound effect of plowing depth and forward speed on performance of tractor-implement systems in plowing process.展开更多
An integrated mechatronic apparatus was developed based on tilting plate method in order to precisely measure static friction coefficient(SFC)and dynamic friction coefficient(DFC)of agricultural products.The apparatus...An integrated mechatronic apparatus was developed based on tilting plate method in order to precisely measure static friction coefficient(SFC)and dynamic friction coefficient(DFC)of agricultural products.The apparatus consisted of two main parts(mechanical and electrical parts).The main element of mechanical part was rotary container.Meanwhile,the electrical part included control,display,goniometer,level controller,rotational power supply,and infrared unit.The apparatus was initially simulated in simulation environment and practically calibrated to achieve high precision measurements.To appraise performance of the apparatus,the SFC and DFC of three grains were measured on five contact surfaces.Experiments were also conducted by means of a typical apparatus operating based on puling force method.Some statistical descriptor parameters such as mean absolute percentage error(MAPE),average of absolute values of measurement errors(AAVME),maximum of absolute values of measurement errors(MAVME),and correlation coefficient were used to compare accuracy of the apparatus with the typical one.The acceptable AAVME(<10%),MAVME(<10%),MAPE(<5%),and correlation coefficient(>0.9)indicated high accuracy,stability,and efficiency of the apparatus for automatic measurements of the SFC and DFC.From practical point of view,the mechatronic apparatus would be a beneficial tool for experimental,educational,demonstrational,and research works.展开更多
This paper proposes a calculator for estimation of drawbar pull supplied bymechanical front wheel drive tractor based on nominal input variable of tractor drivingmode in two-wheel drive(2WD)and four-wheel drive(4WD),a...This paper proposes a calculator for estimation of drawbar pull supplied bymechanical front wheel drive tractor based on nominal input variable of tractor drivingmode in two-wheel drive(2WD)and four-wheel drive(4WD),and numeral input variables of tractor weight(53.04–78.45 kN)and slip of driving wheels(1.4–15.1%)utilizing intelligent fuzzy systems.The systemswere developed bymeans of various input membership functions,output membership functions,defuzzification methods,and training cycles.The prominent developed system for estimation of the drawbar pull yielded a user-friendly intelligent fuzzy calculator with admissible accuracy(coefficient of determination=0.993).Data obtained from the calculator revealed increasing nonlinear trend of the drawbar pull in range of 12.9–57.5 kN as concurrent augment of slip of the wheels and tractor weight,for 2WD mode.In case of the 4WD mode,it nonlinearly raised from 12.8 to 77.7 kN.Therefore,effect of the slip and weight on the drawbar pull was found synergetic.Moreover,the drawbar pull ranges elucidated that the drawbar pull proliferated as the 4WD mode was employed rather than the 2WD mode.Generally,benchmark of the prominent developed intelligent fuzzy system,not only provide simple calculator with the widest applicability for different tractormodels,but also produces added values in enrichment of realization level in domain of tractor drawbar pull concepts.展开更多
文摘A newcontrolled seed metering unit was designed and mounted on a common grain drill for direct seeding of wheat(DSW).It comprised the following main parts:(a)a variable-rate controlled direct current motor(DCM)as seed metering shaft driver,(b)two digital encoders for sensing the rotational speed of supplemental ground wheel(SGW)and seed metering shaft and(c)a control box to handle and process the data of the unit.According to the considered closed-loop control system,the designed control box regularly checked the revolution per minute(RPM)of seed metering shaft,as operation feedback,using its digital encoder output.The seeding ratewas determined based on the calculated error signal and output signal of the digital encoder of the SGW.A field with four different levels of wheat stubble coverage(10%,30%,40%and 50%)was selected for evaluation of the fabricated seed metering unit(FSMU).The dynamic tests were conducted to compare the performance of installed FSMU on the grain drill and equipped grain drill with common seed metering unit(CSMU)at three forward speeds of 4,6 and 8(Km/h)for DSW.Results of the FSMU assessment demonstrated that an increase in forwardspeed of grain drill(FSGD)and stubble coverage did not significantly affect the seeding rate in the grain drill forDSW.Using theFSMU reduced the coefficient of variation(CV)by approximately 50%.Consequently,applying the FSMU on the common grain drill led to a desirable seeding rate at different forward speeds of the grain drill and stubble existence.
文摘Tractor fuel efficiency parameters(TFEPs)(fuel consumption per working hour(FCWH),fuel consumption per tilled area(FCTA)and specific volumetric fuel consumption(SVFC))were intelligently simulated.A neurocomputing based simulation strategy(adaptive neurofuzzy inference system(ANFIS))was used to simulate the TFEPs.A comparison was also made between results of the best ANFIS environment and those of another neurocomputing based simulation strategy,artificial neural network(ANN).Field experiments were conducted at plowing depths of 10,20 and 30(cm)and forward speeds of 2,4 and 6(km/h)using a disk plow implement.Statistical descriptor parameters applied to evaluate simulation environments indicated that the best simulation environment of both ANFIS and ANN were able to perfectly predict the TFEPs.However,the best comprehensive ANN simulation environment with a simple architecture of 2-6-3 was easier to use than three individual ANFIS simulation environments.The ANN results revealed that simultaneous increase of forward speed from 2 to 6(km/h)and plowing depth from 10 to 30(cm)led to nonlinear increment of the FCWH from 5.29 to 14.89(L/h)and nonlinear decrement of the SVFC from 2.95 to 0.67(L/h kW).Meanwhile,forward speed increment along with plowing depth decrement resulted in nonlinear decrement of the FCTA from 28.13 to 12.24(L/ha).Interaction of forward speed and plowing depth on the FCWH and SVFC was congruent,while it was incongruent for the FCTA.It is suggested to employ the ANN environment in developing future fuel planning schemes of tractor during tillage operations.
文摘This paper deals with implementation of intelligent simulation configurations for prediction of tractor wheel slip in tillage operations.The effects of numeral variables of forward speed(2,4,and 6 km/h)and plowing depth(10,20,and 30 cm),and nominal variable of tractor driving mode(two-wheel drive(2WD)and four-wheel drive(4WD))on tractor rear wheel slip were intelligently simulated utilizing data mining methodologies of artificial neural network(ANN)and adaptive neuro-fuzzy inference system(ANFIS).Neuro-fuzzy potential of the ANFIS simulation framework against neural ability of the ANN simulation framework was apprised.Results confirmed higher efficiency of the best configuration of the ANFIS simulation framework with satisfactory statistical performance criteria of coefficient of determination(0.981),root mean square error(1.124%),mean absolute percentage error(1.515%),and mean of absolute values of prediction residual errors(1.135%)than that of the ANN simulation framework.Physical perception obtained from the ANFIS simulation results demonstrated that the wheel slip increased nonlinearly with increment of forward speed and plowing depth,while it decreased as tractor driving mode changed from the 2WD to 4WD.Therefore,the best configuration of the ANFIS based intelligent simulation framework implemented in this study can be used for further relevant studies of tractor rear wheel slip as a reference.
文摘This study is dedicated to examine predictive ability of neural computing environments,based on artificial neural network(ANN)and adaptive neuro-fuzzy inference system(ANFIS)strategies,for integrated simulation of ultrasound-assisted hydration kinetics of wheat kernel.Hydration process was accomplished at five hydration temperatures of 30,40,50,60 and 70C in ultrasonication conditions named control(without ultrasound treatment),US1(25 kHz,360 W)and US2(40 kHz,480 W).The hydration temperature,ultrasonication condition,and hydration time were used as input variables and moisture content was taken as output variable in the neural computing simulation environments.On account of statistical performance criteria,the distinguished ANFIS simulation environment with coefficient of determination of 0.991,root mean square error of 2.478%d.b.,mean relative deviation modulus of 4.301%and average of absolute values of simulation residual errors of 1.863%d.b.was better performed than the distinguished ANN simulation environment.The ANFIS simulation results showed that individual or simultaneous increment of hydration temperature and hydration time caused nonlinear increment of moisture content at any given ultrasonication condition.Moreover,physical perception obtained from the integrated ANFIS simulation results indicated congruency effect(sponge and acoustic cavitation)of cutting-edge ultrasound technology on water absorption.The ANFIS simulation results improved the state of art in domain of studying ultrasoundassisted hydration process of wheat.Therefore,the distinguished ANFIS simulation environment is suggested to be served as an effective step towards management of ultrasound-assisted hydration process of wheat in seed priming,flour milling(tempering),making dough,and wet storage processes.
文摘Energy indices(energy requirement for tillage implement(ERTI)and tractor overall energy efficiency(TOEE))of tractor-implement during tillage operations were aimed to be investigated in this study.To generate a new comprehensive model,the effects of forward speed at three levels(2,4 and 6 km/h)and plowing depth at three levels(10,20 and 30 cm)on energy indices were experimentally evaluated.Two soft computing techniques,artificial neural network(ANN)and adaptive neuro-fuzzy inference system(ANFIS),were employed to prognosticate energy indices.Comparison between the best developed structure of each soft computing technique demonstrated that one comprehensive ANN model was preferred than two individual ANFIS models.According to the ANN prognostication results,simultaneous increase of forward speed from 2 to 6 km/h along with plowing depth increment from 10 to 30 cm led to nonlinear increment of the ERTI and TOEE from 33.87 to 122.66 MJ/ha and 4.65 to 17.85%,respectively.Moreover,interaction of forward speed and plowing depth on energy indices was congruent.Development of comprehensive ANN model now makes it possible to answer fundamental questions in domain of the effect of plowing depth and forward speed on energy indices of tractor-implement that were previously intractable.Hence,to properly manage energy indices and reduce energy dissipation of tractor-implement,application of the new developed ANN model is strongly recommended.
文摘Friction coefficients(static friction coefficient(SFC)and dynamic friction coefficient(DFC))of pomegranate seed on different structural surfaces(glass,aluminum,plywood,galvanized steel and rubber)as affected by moisture content(4-21.9%(d.b.))and sliding velocity(1.4-16(cm/s))were investigated.Analysis of variance(ANOVA)was performed to determine the effect of main treatments and their interactions on SFC and DFC.Significance of single or multiple effect of the main treatments with five levels was assessed using Duncan’s multiple range test(DMRT).To predict SFC and DFC,multiple linear regression(MLR)modeling technique was applied for each type of structural surface.The goodness of fit of each MLR model was evaluated using statistical parameters:coefficient of determination,root mean square error and mean relative deviation modulus.Results showed that the minimum and maximum SFC or DFC were in minimum and maximum moisture content on glass and rubber surface,respectively.ANOVA table indicated the significant effect of main treatments and their interactions on SFC and DFC at significance level of 1%(P<0.01).According to DMRT results,SFC linearly increased as moisture content increased and DFC increased also linearly as individual or simultaneous increment of moisture content and sliding velocity occurred,for all experimental conditions.According to the obtained statistical parameters,both SFC and DFC were properly predicted by means of MLR modeling technique.
文摘Tendency towards computer simulations linked to agricultural machinery has enormously increased in recent years.In this regard,the principal contribution of current research was to develop soft computing simulation workplaces for performance prognostication of tractor-implement system in plowing process.Two neurofuzzy strategies based on multiple adaptive neuro-fuzzy inference systems(MANFIS)scenario and the MANFIS coupled with multiple nonlinear equations(MNE)scenariowere executed in theworkplace.Additionally,neural strategy based on artificial neural network(ANN)scenario was also fulfilled in the workplace.Operational variables of plowing depth(10–30 cm),forward speed(2–6km/h),and tillage implement type(moldboard,disk,and chisel plow)were considered as theworkplace inputs and ten performance parameters were taken as the workplace outputs.According to the obtained prognostication accuracy,simulation time,and user-friendly configuration of three scenarios(ANN,MANFIS,andMANFIS+MNE),the MANFIS+MNE was recognized as the prominent simulation scenario.According to the MANFIS+MNE workplace results,for each tillage implement,the compound effect of plowing depth and forward speed on some performance parameters(required draft force of implement,tractor rear wheel slip,fuel consumption per working hour,specific volumetric fuel consumption,tractor drawbar power,energy requirement for tillage implement,overall energy efficiency,and tractor tractive efficiency)was nonlinearly synergetic.However,it was nonlinearly antagonism in case of specific draft force and fuel consumption per tilled area.The MANFIS+MNE workplace simulation results provide opportunity for technical farmer associations involved in the decision-making of agricultural machinerymanagement in order to gain exhaustive fundamental insights into the compound effect of plowing depth and forward speed on performance of tractor-implement systems in plowing process.
基金The financial support provided by the Scientific Society of Shiraz University under grant number of 13-93.
文摘An integrated mechatronic apparatus was developed based on tilting plate method in order to precisely measure static friction coefficient(SFC)and dynamic friction coefficient(DFC)of agricultural products.The apparatus consisted of two main parts(mechanical and electrical parts).The main element of mechanical part was rotary container.Meanwhile,the electrical part included control,display,goniometer,level controller,rotational power supply,and infrared unit.The apparatus was initially simulated in simulation environment and practically calibrated to achieve high precision measurements.To appraise performance of the apparatus,the SFC and DFC of three grains were measured on five contact surfaces.Experiments were also conducted by means of a typical apparatus operating based on puling force method.Some statistical descriptor parameters such as mean absolute percentage error(MAPE),average of absolute values of measurement errors(AAVME),maximum of absolute values of measurement errors(MAVME),and correlation coefficient were used to compare accuracy of the apparatus with the typical one.The acceptable AAVME(<10%),MAVME(<10%),MAPE(<5%),and correlation coefficient(>0.9)indicated high accuracy,stability,and efficiency of the apparatus for automatic measurements of the SFC and DFC.From practical point of view,the mechatronic apparatus would be a beneficial tool for experimental,educational,demonstrational,and research works.
文摘This paper proposes a calculator for estimation of drawbar pull supplied bymechanical front wheel drive tractor based on nominal input variable of tractor drivingmode in two-wheel drive(2WD)and four-wheel drive(4WD),and numeral input variables of tractor weight(53.04–78.45 kN)and slip of driving wheels(1.4–15.1%)utilizing intelligent fuzzy systems.The systemswere developed bymeans of various input membership functions,output membership functions,defuzzification methods,and training cycles.The prominent developed system for estimation of the drawbar pull yielded a user-friendly intelligent fuzzy calculator with admissible accuracy(coefficient of determination=0.993).Data obtained from the calculator revealed increasing nonlinear trend of the drawbar pull in range of 12.9–57.5 kN as concurrent augment of slip of the wheels and tractor weight,for 2WD mode.In case of the 4WD mode,it nonlinearly raised from 12.8 to 77.7 kN.Therefore,effect of the slip and weight on the drawbar pull was found synergetic.Moreover,the drawbar pull ranges elucidated that the drawbar pull proliferated as the 4WD mode was employed rather than the 2WD mode.Generally,benchmark of the prominent developed intelligent fuzzy system,not only provide simple calculator with the widest applicability for different tractormodels,but also produces added values in enrichment of realization level in domain of tractor drawbar pull concepts.
