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| 1 | On the neurocomputing based intelligent simulation of tractor fuel efficiency parameters显示文摘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. | S.M.Shafaei M.Loghavi S.Kamgar | 2018 | Information Processing in Agriculture2018，5，2： | 5 |
| 2 | Feasibility of implementation of intelligent simulation configurations based on data mining methodologies for prediction of tractor wheel slip显示文摘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. | S.M.Shafaei M.Loghavi S.Kamgar | 2019 | Information Processing in Agriculture2019，6，2： | 4 |
| 3 | Prognostication of energy indices of tractor-implement utilizing soft computing techniques显示文摘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. | S.M.Shafaei M.Loghavi S.Kamgar | 2019 | Information Processing in Agriculture2019，6，1： | 3 |
| 4 | Reliable execution of a robust soft computing workplace found on multiple neuro-fuzzy inference systems coupled with multiple nonlinear equations for exhaustive perception of tractor-implement performance in plowing process显示文摘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. | S.M.Shafaei M.Loghavi S.Kamgar | 2019 | Artificial Intelligence in Agriculture2019，，2： | 2 |
| 5 | Benchmark of an intelligent fuzzy calculator for admissible estimation of drawbar pull supplied by mechanical front wheel drive tractor显示文摘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. | S.M.Shafaei M.Loghavi S.Kamgar | 2020 | Artificial Intelligence in Agriculture2020，，1： | 0 |
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