The Evaluation and Development of a Prediction Artificial Neural Network Model for Specific Volumetric Fuel Efficiency (SVFE) of a Tractor–Chisel Plow System Based on Field Operation

For every tractor test carried out on a concrete road under defined conditions, the Nebraska Tractor Test Laboratory (NTTL) provides values of the specific volumetric fuel efficiency (SVFE) in unit of kWh/L). Because soil tillage is a highly energy-intensive process and the energy consumption of tillage operations is a significant component of a farm budget, there is a growing amount of attention being given to the examination of the SVFE for tillage operations. Nonetheless, the study of the tillage process and a scientific approach to the tillage process are becoming more and more dependent on scientific modeling. Therefore, in this study based on real-tillage field operation, an artificial neural network (ANN) model was built to predict SVFE. This study aimed to confirm that the ANN model could incorporate 10 inputs for prediction: initial soil moisture content, draft force, initial soil bulk density, sand, silt, and clay proportions in the soil tractor power, plow width, tillage depth, and tillage speed. The Qnet v2000, as an ANN simulation software, was employed for the simulation of the SVFE. In this regard, 20,000 runs of Qnet v2000 were completed for the training and testing stages. The anticipated results displayed that the determination coefficient (R2) was larger than 0.96; using the training dataset, R2 was 0.982 and using the testing dataset, R2 was 0.9741, indicating that the recognition of a full ANN model makes it likely to reply to essential enquiries that were previously unanswerable regarding the impact of working and soil conditions on the SVFE of a tractor–tillage implement system. Additionally, sensitivity analyses were completed to specify which modeled parameters were more sensitive to the factors using the obtained ANN model. According to the sensitivity analysis, SVFE was more affected by changes in the tillage speed (21.07%), silt content in the soil (15.56%), draft force (11.01%), and clay content in the soil (10.86%). Predicting SVFE can lead to more appropriate decisions on tractor–chisel plow combination management. Therefore, it is highly advisable to use the newly created ANN model to appropriately manage SVFE to reduce tractor–tillage implement energy dissipation. Additionally, suitable management of some variables, for example, tillage depth, tillage speed, and soil moisture content, can help enhance fuel consumption in the tractor–tillage implementation system.