K-NEAREST NEIGHBORS METHOD FOR PREDICTION OF FUEL CONSUMPTION IN TRACTOR-CHISEL PLOW SYSTEMS

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.

摘要 绝大多数核心农业作业均需消耗大量能源，这部分开支占据农场预算的主要份额。据此，针对不同规模的农业作业分析农业机械的燃油消耗，能够实现燃油消耗预测，进而制定合理的能源预算。本研究的核心目标是验证k近邻（k-nearest neighbors, KNN）算法对拖拉机-凿式犁系统燃油消耗的准确预测能力。研究从已发表文献中获取173组数据，选取其中139组作为训练集，剩余34组作为测试集。输入参数涵盖拖拉机功率、耕作宽度、耕作深度与耕作速度、土壤砂粒、粉粒、黏粒占比、初始土壤含水率以及初始土壤容重。将KNN方法的预测性能与多元线性回归（multiple linear regression, MLR）进行对比，并通过实验数据验证两种方法的预测能力。结果表明，KNN方法的表现优于多元线性回归方法：当k=2时，KNN方法的测试集相关系数为0.817，而多元线性回归方法的测试集相关系数为0.422。本研究证实，当取k=2（即选取2个最近邻样本）时，KNN方法适用于在所研究参数范围内估算拖拉机-凿式犁系统的燃油消耗。