Prediction of Specific Fuel Consumption of 60 HP 2WD Tractor Using Artificial Neural Networks

Küçüksariyildiz, Hanifi; Çarman, Kazım; Sabancı, Kadir

doi:10.30939/ijastech..1010318

Cited by 5 publications

(4 citation statements)

References 12 publications

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“…The best structure was obtained with the architecture of 6-10-1 with the Levenberg-Marquart training algorithm with the R 2 value of 0.935 and MSE of 0.0127. Küçüksarıyıldız et al [21] predicted the specific fuel consumption of a 60 hp front-wheel drive tractor using an artificial neural network. The independent parameters included the dynamic load on the axle (1796, 2076, 2276, and 2476 daN), tire inflation pressure (80, 120, and 160 KPa), and traction force (500, 1000, 1500, and 2000 daN).…”

Section: Specific Fuel Consumptionmentioning

confidence: 99%

Use of a Convolutional Neural Network for Predicting Fuel Consumption of an Agricultural Tractor

et al. 2023

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The energy crisis and depleting fossil fuel resources have always been the focus of researchers. Fuel consumption of agricultural tractors is not an exception. Researchers have used different methods to predict fuel consumption. With the development of artificial intelligence in the last decade, all re-searchers’ attention has been directed towards it. Deep learning is a subset of machine learning, which was inspired by the data processing patterns in the human brain. The deep learning method has been used in research due to the advantages of high accuracy and generalization. So far, no research has used this method to predict fuel consumption. In this research, field experiments were carried out in sandy clay loam and clay soils to model the temporal fuel consumption and specific fuel consumption of an agricultural tractor using a convolutional neural network (CNN), while having some parameters such as the soil type, soil conditions, tool parameters, and operation pa-rameters. The experiments were conducted within each soil texture in a factorial manner based on the randomized complete block design (RCBD) with three replicates. For each soil texture, various moisture levels (8–17% for dry and 18–40% for moist soils), tractor forward speeds (1.2, 1.6, 1.8, and 2.2 km h−1), working depths (30 and 50 cm), the number of passes (2 and 6), and tire inflation pressure (20 and 25 psi) were selected, and cone index, dynamic load, and moisture content were measured in each experimental section. The designed networks used to predict the instant fuel consumption were of a CNN type. The results indicated that the network developed based on the Sgdm algorithm outperformed the others, and thus it was selected for modeling purposes. The network was evaluated based on R2 and MSE criteria. For the temporal fuel consumption, the best results were obtained while using 8-510-510-1 architecture with R2 = 0.9729 and MSE = 0.0049. The 8-100-95-1 architecture also led to the best prediction of the specific fuel consumption with R2 of 0.9737 and MSE of 0.0054. The high prediction accuracy and low error in this research compared to previous studies indicate the superiority of this method in order to predict fuel consumption. It was also observed from the results that the input parameters, which include soil, tool, and operational parameters, are all effective on fuel consumption. Proper management of some parameters, such as working depth, tire inflation pressure, and forward speed, can help to optimize fuel consumption.

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Section: Specific Fuel Consumptionmentioning

confidence: 99%

Use of a Convolutional Neural Network for Predicting Fuel Consumption of an Agricultural Tractor

et al. 2023

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“…The specific fuel consumption is one of the tractor performance indicators, thus, Hanifi et. al., (2021) [119] proposed NN to predict fuel consumption of 60 HP 2WD tractor.…”

Section: Tractionmentioning

confidence: 99%

Application of Computational Intelligence Methods in Agricultural Soil-Machine Interaction : A Review

Badgujar¹,

Figueroa²,

Das³

et al. 2022

Preprint

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Soil working tools, implements, and machines are inevitable in mechanized agriculture. The soil-tool/machine interaction is a multivariate, dynamic, and intricate process. The accurate interpretation, description, and modeling of a soil-machine interaction is key to providing a solution to sustainable crop production by reducing energy input, excessive soil pulverization, and compaction. The traditional method provides insight into soil-machine interaction but often provides inadequate solutions and lacks broad applicability. Computational intelligence (CI) is a comprehensive class of approaches that rely on approximate information to solve complex problems. The CI method has been extensively studied and applied in soil tillage and traction domain in recent decades. The study critically reviews the CI techniques implemented in soil-machine interactions, especially in the context of tillage, traction, and compaction. The traditional methods and their limitation are discussed. The fundamental of CI methods and a detailed overview of the most popular methods are provided. The study reviews and summarizes the 50 selected articles on soil-machine interaction studies where CI methods were employed. It discusses the strength and limitations of employed CI methods. It also suggests the emergent CI methods and future applications are discussed. The outlined study would serve as a concise reference and a quick and systematic way to understand the applicable CI methods that allow crucial farm management decision-making.

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“…They proposed that the devised ANN model could be used to develop future tractor fuel-planning schemes during tillage operations. Küçüksarıyıldız et al [21] used an ANN model to calculate the precise fuel consumption at various axle loads, the inflation pressure of tractor tires, and the drawbar force for a 60 hp tractor. By experimenting with various transfer functions, the hidden-layer neuron count, and training algorithms, they discovered the most effective ANN model.…”

Section: Introductionmentioning

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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Prediction of Specific Fuel Consumption of 60 HP 2WD Tractor Using Artificial Neural Networks

Cited by 5 publications

References 12 publications

Use of a Convolutional Neural Network for Predicting Fuel Consumption of an Agricultural Tractor

Use of a Convolutional Neural Network for Predicting Fuel Consumption of an Agricultural Tractor

Application of Computational Intelligence Methods in Agricultural Soil-Machine Interaction : A Review

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

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