Abdulwahed M. Aboukarima

In mechanized agricultural activities, fuel is particularly important for tillage operations. In this study, the impact of seven distinct parameters on fuel usage per unit of draft power was examined. The parameters are tractor power, soil texture index, plowing speed, plowing depth, width of implement, and both initial soil moisture content and soil bulk density. This study investigated the construction of an artificial neural network (ANN) model for tractor-specific fuel consumption predictions for two tillage implements: chisel and moldboard plows. The ANN model was created based on the collection of related data from previous research studies, and the validation was performed using actual field experiments in clay soil using a chisel plow. The developed ANN model (9-22-1) was confirmed by graphical assessment; additionally, the root-mean-square error (RMSE) was computed. Based on the RMSE, the results demonstrated a good agreement for specific fuel consumption per draft power between the observed and predicted values, with corresponding RMSE values of 0.08 L/kWh and 0.075 L/kWh for the training and testing datasets, respectively. The novelty of the work presented in this paper is that, for the first time, a farm machinery manager can optimize tractor fuel consumption per draft power by carefully controlling certain parameters, such as initial soil moisture content, tractor power, plowing speed, implement width, and depth of plowing. The results show that the input parameters make a significant contribution to the output over the used data with different percentages. Accordingly, the contribution analysis showed that the implement width had a high impact on tractor-specific fuel consumption for both plows at 30.13%; additionally, the chisel and moldboard plows contributed 4.19% and 4.25% in predicting tractor fuel consumption per draft power. This study concluded that practical useful advice for agricultural production can be achieved through optimizing fuel consumption rate by selecting the proper levels of affecting parameters to reduce fuel costs. Moreover, an ANN model could be used to develop future tractor fuel-planning schemes for tillage operations.

The famous empirical model for the horizontal force estimation of farm implements was issued by the American Society of Agricultural Biological Engineers (ASABE). It relies on information on soil texture through its soil texture adjustment parameter, which is called the Fi -parameter. The Fi-parameter is not measurable, and the geometry of the plow through the machine parameter values are not measurable; however, the tillage speed, implement width, and tillage depth are measurable. In this study, the Fi-parameter was calibrated using a regression technique based on a soil texture norm that combines the sand, silt, and clay contents of a soil with R2 of 0.703. A feed-forward artificial neural network (ANN) with a backpropagation algorithm for training purposes was established to estimate the modified values of the horizontal force based on four inputs: working field criterion, soil texture norm, initial soil moisture content, and the horizontal force (which was estimated by the ASABE standard using the new—Fi-parameter). Our developed ANN model had high values for the coefficient of determination (R2) and their values in the training, testing, and validation stages were 0.8286, 0.8175, and 0.8515, respectively that demonstrated the applicability for the prediction of the modified horizontal forces. An Excel spreadsheet was created using the weights of the established ANN model to estimate the values of the horizontal force of specific tillage implements, such as a disk, chisel, or moldboard plows. The Excel spreadsheet was tested using data for a moldboard plow; in addition, a good prediction of the required horizontal force with a percentage error of 10% was achieved. The developed Excel spreadsheet contributed toward a numerical method that can be used by agricultural engineers in the future. Furthermore, we also concluded that the equations presented in this study can be formulated by any of computer language to create a simulation program to predict the horizontal force requirements of a tillage implement.