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.
KEYWORDS
Machine-learning algorithms; tillage; prediction; k-nearest neighbors; fuel consumption; chisel plow
