Cloud-driven serverless framework for generalised tractor fuel consumption prediction model using machine learning

Nagar, Harsh; Machavaram, Rajendra; Ambuj,; Soni, Peeyush; Mahore, Vijay; Patidar, Prakhar

doi:10.1080/23311916.2024.2311810

Cited by 4 publications

(1 citation statement)

References 30 publications

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“…However, this difference may be attributed to the values of draft power; thus, this demonstrates the ability of the created ANN model to predict tractor-specific fuel consumption per draft power. However, for professionals to evaluate the vehicle's economy and to provide precise information regarding fuel utilization, an accurate fuel consumption model is essential [64].…”

Section: Analysis Of the Developed Ann Model Using Validation Datasetmentioning

confidence: 99%

Prediction of Specific Fuel Consumption of a Tractor during the Tillage Process Using an Artificial Neural Network Method

Al-Sager,

Almady,

Marey

et al. 2024

Agronomy

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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.

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Section: Analysis Of the Developed Ann Model Using Validation Datasetmentioning

confidence: 99%

Prediction of Specific Fuel Consumption of a Tractor during the Tillage Process Using an Artificial Neural Network Method

Al-Sager,

Almady,

Marey

et al. 2024

Agronomy

View full text Add to dashboard Cite

show abstract

Explore driving factors to energy consumption of electric vehicles based on structural equation modeling

Zubair,

Chen,

et al. 2024

International Journal of Green Energy

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AI-based engine performance prediction cum advisory system to maximise fuel efficiency and field performance of the tractor for optimum tillage

Nagar,

Machavaram,

Kulkarni

et al. 2024

Systems Science & Control Engineering

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Cloud-driven serverless framework for generalised tractor fuel consumption prediction model using machine learning

Cited by 4 publications

References 30 publications

Prediction of Specific Fuel Consumption of a Tractor during the Tillage Process Using an Artificial Neural Network Method

Prediction of Specific Fuel Consumption of a Tractor during the Tillage Process Using an Artificial Neural Network Method

Explore driving factors to energy consumption of electric vehicles based on structural equation modeling

AI-based engine performance prediction cum advisory system to maximise fuel efficiency and field performance of the tractor for optimum tillage

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