## 3 Results and Discussion
### Performance of the Development of the ANN Model
In the training, testing, and validation datasets, the performance of the anticipated values for the modified horizontal  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Inputs**} & \multicolumn{3}{c}{**Output**} \\ \hline
**WFC:** **Combined** **Different** **Variables (-)** & **STN:** **Representing** **Soil Texture (-)** & **MC:** **Soil Moisture** **Content (\% db)** & **DC:** **ASABE form by the Modified** **Modified** **Modified** **Fire-Parameter (kN)** & **Measured: Draft Force** **Measured in the Field** **Experiments or from** **Previous Studies (kN)** & **Predicted: Modified** **Modified** **Horizontal Force** **Hor  
force is shown in Figure 3. In addition, the accuracy of the ANN model's predictions can be evaluated by comparing the predicted horizontal force with measured values that are determined through the field measurements. Additionally, the model's accuracy can be improved by adjusting the model's hyper parameters and optimizing the training process [80]. Overall, an ANN model can be a useful tool for predicting the modified horizontal force for the selected tillage implements and for ensuring that the established model is suitable for frame machinery management purposes. Figure 3: Relationship between the measured horizontal force and predicted modified horizontal force (MHF) using the training, testing, and validation datasets. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **R2** & **RMSE, kN** & **MAE, kN** & **No. of Data Points** \\ \hline Training & 0.8286 & 0.733 & 0.255 & 263 \\ Testing & 0.8175 & 2.105 & 1.349 & 57 \\ Validation & 0.8515 & 2.523 & 2.155 & 57 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Performance evaluation parameters for predicting the modified horizontal force of the established ANN for structure 4-20-1. The use of ANN models for predicting the draft force of tillage implements can save time and resources by providing quick and accurate predictions for energy requirements without the need for extensive field testing. It can also help farmers make informed decisions about matching a mechanization unit. However, there are also limitations in using ANN models for predicting the draft force of tillage implements and other parameters. The accuracy of the ANN model is contingent on the quality and quantity of the training data used [80]. Additionally, the model's accuracy may decrease when applied to data with significant differences from the training dataset [80]. Various studies are available on the draft force, which is modeled by ANN method, for tillage implements [81]; however, the former ANN modeling approaches did not attempt to modify the draft force of ASABE draft form [12]. Hence, the authors of this paper presented this applied ANN model for predicting the modified draft force of the famous primary tillage implements that are used for farm machinery management purposes. Compared to other studies, the present work stands alone with a novel approach; it has significant value in formulating the soil Fi-parameter used by ASABE [12] for the purposes of draft determination and for modifying the famous draft force that is detailed in the ASABE form [12]. In conclusion, ANN modeling proved to be a valuable tool for predicting the modified draft force of certain primary tillage tools and for ensuring that the prediction data are suitable for farm machinery management. However, the model's limitations must be considered when interpreting its results. Hence, the results of this study should be used as a complement to traditional field-testing rather than as a replacement. There was no limitation of the investigated ANN model to different types of farm implements. However, the potential challenges or variations in the performance will depend on the quality and quantity of the training data used.