[40] estimated tractor power take-off (PTO) performance using a backpropagation ANN model. Twenty dissimilar variables were considered as the inputs for a PTO tractor performance expectation. The data that were employed as inputs to train the ANN model were acquired from 141 reports that belonged to tractor test procedures, and which were run between year 1997 and year 2013. The optimum ANN model structure was assigned by a trial-and-error procedure, and 30 different ANN structures were tried. For prediction, an ANN model with two hidden layers that had 40 and 35 nodes in both the first and second hidden layers, respectively, gave the maximum performance. The ANN model can construct a simulation of a tractor's performance; it also allows the optimal setting of dissimilar variables, as well as can improve the decision making of a producer in the proposal of new tractor. Furthermore, scientific revisions showed that usual regression methods might produce biased outcomes, as they are unable to gauge the multicollinearity between independent variables [41]. Therefore, a crucial and optimal selection would be to use ANN, which classifies the linear and non-linear ways while fitting statistical models on to the original data [42]. To the best of our knowledge, despite extensive literature examination, no study on the prediction of the soil texture adjustment parameter (Fi) was used in ASABE form [12]. In addition, this method being used to modify the values of the draft force of tillage implements, as estimated from the form of ASABE [12] by using an ANN model, was also not tested yet. The importance of conducting this research lies in the fact that the draft force is a vital factor in determining the energy requirements of tillage implements for various purposes, including cost analysis, fuel consumption prediction, and matching agricultural tractors with the correct tillage implements. The ability to accurately predict the modified draft force of tillage implements, based on ASABE form [12], through the use of machine learning models such as ANN can help in producing better farm machinery management and utilization of the available resources. Furthermore, an accurate prediction draft force can also aid in mitigating the potential of using tillage implement management approaches that are associated with consuming diesel fuel, which is a significant concern in many parts of the world. By accurately predicting the draft force of tillage implements, farm machinery engineers can determine the variables that will offer less draft force for tillage implements when conducting tillage operation on a specific soil texture. This is possible because the user can change tractor power, tillage speed, tillage depth, implement width, soil bulk density, and soil moisture content as needed to achieve a lower energy for the plowing process. Conducting this research in the study area is necessary because the draft force of tillage implements can vary significantly depending on the specific soil texture and other factors. Therefore, it is essential to develop models that are tailored to the unique characteristics of the study area to achieve accurate predictions, or so that they can be used in many parts of the world. Moreover, the novelty of the paper lies in developing a new soil texture parameter that can address the effect of soil texture on the draft force of tillage implements.