The results indicated that the percentage of relative error fluctuated from 8% to 14%. [PERSON] et al. [20] used Equation (1) to validate the precision of their actual-time tillage depth determination by establishing a regression model between the measured horizontal force and the predicted data. The accuracy was assessed by the coefficient of determination (R\({}^{2}\)), which was around 0.715. Other researchers offered various applications for objectifying the usefulness of using the ASABE form [12] in establishing computer programs, which are frequently used to support farm machinery managers in decision making regarding how to select machinery and power requirements [21]. Researchers use different types of the force meter to acquire the forces acting on the tillage implements during the plowing process; in particular, the draft force [22]. Others applied different statistical approaches and models to predict the draft force acting on the agricultural implements [23; 24; 25; 26; 27; 28; 29]. However, to compensate for the shortcomings of using the ASABE model [12], researchers developed and evaluated intelligent computing methods, such as artificial neural network models and fuzzy knowledge-based models,which led to greater progress in the application of many technologies. In addition, these developments provide the possibility to solve complex agricultural engineering problems, particularly the draft force prediction of agricultural implements [30; 31; 32; 33; 34; 35; 36; 37]. After entering the relevant parameters for the implement, soil, and working conditions, these intelligent models could provide a straight estimate of the draft force of a tillage implement. However, to train these models, experimental values had to be acquired from a field experiment or a soil bin experiment. Additionally, the intelligent models did not take into account the connection between the draft force and the input parameters that are intended to improve the comprehension of the tillage process and mechanism [10]. [PERSON] et al. [32] prepared an ANN model with a backpropagation algorithm to estimate the horizontal force required to pull a disk flow. The proposed ANN model used ten input variables: tillage depth, tillage forward speed, sand contents in the soil, clay contents in the soil, silt contents in the soil, disk configurations such as disk diameter; disk angle, tilt angle, soil bulk density, and soil moisture content. The coefficient of determination for the validating points of the ANN model were changed between 0.915 and 0.934. [PERSON] et al. [37] employed an ANN model with a backpropagation algorithm to estimate the horizontal force required by a chisel flow possessing a two-winged share in a loam-textured soil. The input variables were tillage depth, forward speed, and the width of the wing share. The ANN model estimated the horizontal force with a small error associated with the measured values. [PERSON] and [PERSON] [38] established an ANN model to estimate the horizontal force of agricultural implements. However, they employed soil moisture content, soil texture, soil compaction level, horizontal deformation, and vertical load as input variables in their ANN model. The coefficient of determination was 0.945 for the horizontal force determination in the model with the testing dataset. [PERSON] et al. [39] set up an ANN model employing the backpropagation algorithm to predict the specific draft force requirements of a moldboard plow in a clay loam soil; this was achieved by using tillage depth and plowing speed as the input layer, and the specific draft force requirements of moldboard plow as the output layer. This was an attempt to use intelligent algorithms to predict the draft requirement of the tillage implements, with an error of less than 1% when compared to the measured draft values. Further research work was required to demonstrate the generalized value of the developed ANN in other soil conditions. Furthermore, [PERSON] et al.