## 4 Conclusions  
In this study, the Fi-parameter--which is not measurable and assumed to describe soil texture in the famous empirical model created by the American Society of Agricultural Biological Engineers (ASABE) for the horizontal force estimation of tillage implements--was calibrated using a regression technique based on soil texture norms; it combined the sand, \begin{table}
\begin{tabular}{c c c} \hline
**Variable** & **Value** & **Unit** \\ \hline Tractor power & 40 & (kW) \\ Sand percentage & 25 & (\%) \\ Silt percentage & 15 & (\%) \\ Clay percentage & 60 & (\%) \\ Soil texture & Clay & (-) \\ Soil texture norm & 0.705655 & (Dimensionless) \\ Working field criterion & 0.062147 & Dimensionless \\ Initial soil moisture content & 13 & (\% db) \\ Soil bulk density & 1.35 & (g/cm\({}^{3}\)) \\ Tillage depth & 18 & (cm) \\ Tillage speed & 5 & (km/h) \\ Implement width & 1.08 & (m) \\ New—Fi-parameter & 1.000647 & (Dimensionless) \\ Measured horizontal force & 11.01 & (kN) \\ Horizontal force determined using Equation & 12.68 & (kN) \\ (1) and new—Fi-parameter (DD) & & \\ Predicted MHF using ANN model based on & 9.91 & (kN) \\ the created Excel spreadsheet & & \\ \hline \end{tabular}
\begin{tabular}{c} \(\text{PE}=\frac{\left|\left(9.91-11.01\right)\right|}{11.01}\times 100=10\%\)  
\end{table} 
Table 10: The data for moldboard flow use for estimating the modified horizontal force in the created Excel spreadsheet. Figure 5: Screen capture of worksheet templet for the determination of the modified horizontal force using ANN model. silt, and clay contents in a soil with an R\({}^{2}\) of 0.703. The purpose was to measure the Fi-parameter to modify the empirical model issued by ASABE to give accurate draft values for tillage implements. Additionally, a set of variables--tractor power, plowing speed, initial bulk density, implement width, and tillage depth--were formulated into one variable, which was labeled the working field criterion, to represent working conditions. The relationships between the working field criterion, the soil texture norm, the initial soil moisture content, the horizontal force estimated from the ASABE model based on the new Fi-parameter as independent variables, and the modified horizontal force as a dependent variable, were all accurately and mathematically modeled by an ANN with a configuration of 4-20-1. The results suggested that establishing an ANN model as an operative tool can be used for accurately predicting draft force of tillage implements in this study: R\({}^{2}\) value of 0.8515, MAE value of 2.155 kN, and RMSE value of 2.523 kN). This was achieved by using the validation dataset of the modified horizontal force for certain tillage implements such as chisel, moldboard, and disk flows. The relative contribution of Input variables was assessed using the established ANN model. The modified horizontal force was most significantly influenced by the working field criterion and horizontal force by 28.05% and 36.10%, respectively (as was calculated by the ASABE form when using the new--Fi-parameter). It should be underlined that the selection of suitable parameters (i.e., tractor power, tillage depth, tillage speed, and implement width) are essential for the effective tillage management of certain soil parameters such as texture, bulk density, and moisture content. Through using the developed equations based on the extracted weights from the trained ANN model, an Excel spreadsheet was created. This spreadsheet can be used to manage the variables that will produce a lesser draft force for tillage implements when they are being used to conduct tillage operations on a specific soil texture. This is possible, as the user can change the tractor power, tillage speed, tillage depth, implement width, and the soil bulk. Additionally, the developed Excel spreadsheet contributes a numerical method that can be used by agricultural engineers in the future.