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... acceleration, and cohesion force, the mass of implement, bulk density, moisture contents, and soil penetration resistance taken for experimental investigation. The 235-6 m circular spring balance that hangs with a capacity of 100 kg and accuracy of 500 g connected between the beam and the centre of the yoke was used to measure the draft force (Fig. ...
Citations
... On a farm, the tractor and plow used for soil tillage are thought to be the main energy users and cost factors [53,54]. Thus, for power savings, the tillage unit (tractor and plow) must be used in proper combination [55]. As a result, users and manufacturers alike need to have access to information about the behavior and activity of plowing units [56,57]. ...
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.
... Ploughing units (tractor and plough) for soil tillage are considered the primary power consumer and one of the cost factors on a farm (Ahmadi, 2018;Oduma et al., 2022). Consequently, an appropriate combination of the tillage unit (tractor and plough) is essential for power saving (Gebre et al., 2023). Information related to the action and behaviour of the ploughing unit is essential and indispensable for both users and manufactures (Fawzi et al., 2021;He et al., 2023). ...
The specific objective of this study is to find a suitable artificial neural network model for estimating the operation indicators
(disturbed soil volume, effective field capacity, draft force, and energy requirement) of ploughing units (tractor disc) in various
soil conditions. The experiment involved two different factors, i.e., (Ι) soil texture index and (ΙΙ) field work index, and included soil
moisture content, tractor engine power, soil bulk density, tillage speed, tillage depth, and tillage width, which were linked to one
dimensionless index. We assessed the effectiveness of artificial neural network and multiple linear regression models between
the values predicted and the actual values using the mean absolute error criterion to test data points. When the artificial neural
network model was applied, the mean absolute error values for disturbed soil volume, effective field capacity, draft force, and
energy requirement were 69.41 m3·hr-1, 0.04 ha·hr-1, 1.24 kN, and 1.95 kw·hr·ha-1, respectively. In order to evaluate the behaviour of
new models, the coefficient R2 was used as a criterion, where R2 values in artificial neural network were 0.9872, 0.9553, 0.9948, and
0.9718, respectively, for the aforementioned testing dataset. Simultaneously, R2 values in multiple linear regression were 0.7623,
0.696, 0.492, and 0.5572, respectively, for the same testing dataset. Based on these comparisons, it was clear that predictions using
the artificial neural network models proposed are very satisfactory.
... Ploughing units (tractor and plough) for soil tillage are considered the primary power consumer and one of the cost factors on a farm (Ahmadi, 2018;Oduma et al., 2022). Consequently, an appropriate combination of the tillage unit (tractor and plough) is essential for power saving (Gebre et al., 2023). Information related to the action and behaviour of the ploughing unit is essential and indispensable for both users and manufactures (Fawzi et al., 2021;He et al., 2023). ...
The specific objective of this study is to find a suitable artificial neural network model for estimating the operation indicators (disturbed soil volume, effective field capacity, draft force, and energy requirement) of ploughing units (tractor disc) in various soil conditions. The experiment involved two different factors, i.e., (Ι) soil texture index and (ΙΙ) field work index, and included soil moisture content, tractor engine power, soil bulk density, tillage speed, tillage depth, and tillage width, which were linked to one dimensionless index. We assessed the effectiveness of artificial neural network and multiple linear regression models between the values predicted and the actual values using the mean absolute error criterion to test data points. When the artificial neural network model was applied, the mean absolute error values for disturbed soil volume, effective field capacity, draft force, and energy requirement were 69.41 m ³ ·hr ⁻¹ , 0.04 ha·hr ⁻¹ , 1.24 kN, and 1.95 kw·hr·ha ⁻¹ , respectively. In order to evaluate the behaviour of new models, the coefficient R ² was used as a criterion, where R ² values in artificial neural network were 0.9872, 0.9553, 0.9948, and 0.9718, respectively, for the aforementioned testing dataset. Simultaneously, R ² values in multiple linear regression were 0.7623, 0.696, 0.492, and 0.5572, respectively, for the same testing dataset. Based on these comparisons, it was clear that predictions using the artificial neural network models proposed are very satisfactory.
