Abstract
ABSTRACT Most important farm operations require a significant amount of energy, and this consumes a major portion of the farm's budget. Consequently, analyzing the fuel consumption of agricultural machinery for farm operations of different sizes makes it possible to predict fuel consumption to set an appropriate budget for energy. The main purpose of this study was to determine the ability of the k-nearest neighbors (KNN) algorithm to predict the fuel consumption of tractor–chisel plow systems correctly. A training-set design of 139 points of 173 data points obtained from the literature was utilized, and the remaining 34 data points were applied as a test set. The input parameters were tractor power, plowing width, depth and speed of plowing, soil percentages of sand, silt, and clay, initial soil moisture content, and initial soil bulk density. The predictive power of the KNN method was compared with that of multiple linear regression (MLR), and experimental data were used to determine the predictive power of both methods. The KNN method generated better results than the multiple linear regression method. The test dataset correlation coefficients were 0.817 for the KNN (k = 2) method and 0.422 for the multiple linear regression method. This study suggests that the KNN method with k = 2 (two nearest neighbors) is suitable for estimating the fuel consumption of tractor–chisel plow systems for input values within the studied range.
Highlights
Developing the ability to predict fuel consumption of tractor–machinery systems is extremely beneficial for farms for budgeting and management; fuel consumption is measured by the amount of fuel used during a specific time period (Grisso et al, 2010)
Considerable research has addressed the prediction of fuel consumption during the tillage of selected regions with solutions using traditional, statistical, and modern computational methods, or combinations among those literature results (Karparvarfard & Rahmanian-Koushkaki, 2015; Tayel et al, 2015; Almaliki et al, 2016; Borges et al, 2017; Ranjbarian et al, 2017)
Skewness is a lack of symmetry in a probability distribution, and kurtosis is the measurement of separation of smoothing probability distribution from a normal distribution shape (Everitt & Skrondal, 2010)
Summary
Developing the ability to predict fuel consumption of tractor–machinery systems is extremely beneficial for farms for budgeting and management; fuel consumption is measured by the amount of fuel used during a specific time period (Grisso et al, 2010). Predicting tractor fuel consumption can lead to more decisions that are appropriate for tractor management (Karparvarfard & Rahmanian-Koushkaki, 2015). Predictive models capable of forecasting the fuel consumption of tractor–machinery systems under different conditions can help farmers optimize their fuel expenditure. Considerable research has addressed the prediction of fuel consumption during the tillage of selected regions with solutions using traditional, statistical, and modern computational methods, or combinations among those literature results (Karparvarfard & Rahmanian-Koushkaki, 2015; Tayel et al, 2015; Almaliki et al, 2016; Borges et al, 2017; Ranjbarian et al, 2017). Successful prediction of the fuel consumption of tractor–chisel systems can aid in selecting tractors that minimize the cost of fuel for tillage
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