Kim Wan-Soo, Lee Dae-Hyun, Kim Yong-Joo, Kim Yeon-Soo, Park Seong-Un
Department of Biosystems Machinery Engineering, Chungnam National University, Daejeon 34134, Korea.
Department of Smart Agricultural Systems, Chungnam National University, Daejeon 34134, Korea.
Sensors (Basel). 2021 Mar 11;21(6):1989. doi: 10.3390/s21061989.
The objective of this study was to develop a model to estimate the axle torque (AT) of a tractor using an artificial neural network (ANN) based on a relatively low-cost sensor. ANN has proven to be useful in the case of nonlinear analysis, and it can be applied to consider nonlinear variables such as soil characteristics, unlike studies that only consider tractor major parameters, thus model performance and its implementation can be extended to a wider range. In this study, ANN-based models were compared with multiple linear regression (MLR)-based models for performance verification. The main input data were tractor engine parameters, major tractor parameters, and soil physical properties. Data of soil physical properties (i.e., soil moisture content and cone index) and major tractor parameters (i.e., engine torque, engine speed, specific fuel consumption, travel speed, tillage depth, and slip ratio) were collected during a tractor field experiment in four Korean paddy fields. The collected soil physical properties and major tractor parameter data were used to estimate the AT of the tractor by the MLR- and ANN-based models: 250 data points were used for developing and training the model were used, the 50 remaining data points were used to test the model estimation. The AT estimated with the developed MLR- and ANN-based models showed agreement with actual measured AT, with the R value ranging from 0.825 to 0.851 and from 0.857 to 0.904, respectively. These results suggest that the developed models are reliable in estimating tractor AT, while the ANN-based model showed better performance than the MLR-based model. This study can provide useful results as a simple method using ANNs based on relatively inexpensive sensors that can replace the existing complex tractor AT measurement method is emphasized.
本研究的目的是基于相对低成本的传感器，开发一种使用人工神经网络（ANN）来估算拖拉机轴扭矩（AT）的模型。事实证明，ANN在非线性分析中很有用，并且与仅考虑拖拉机主要参数的研究不同，它可用于考虑诸如土壤特性等非线性变量，因此模型性能及其应用范围可以扩展到更广泛的领域。在本研究中，将基于ANN的模型与基于多元线性回归（MLR）的模型进行比较以验证性能。主要输入数据为拖拉机发动机参数、主要拖拉机参数和土壤物理性质。在韩国四个稻田的拖拉机田间试验期间，收集了土壤物理性质（即土壤含水量和圆锥指数）和主要拖拉机参数（即发动机扭矩、发动机转速、比油耗、行驶速度、耕作深度和滑移率）的数据。收集到的土壤物理性质和主要拖拉机参数数据用于通过基于MLR和ANN的模型估算拖拉机的AT：使用250个数据点来开发和训练模型，其余50个数据点用于测试模型估算。用开发的基于MLR和ANN的模型估算的AT与实际测量的AT相符，R值分别在0.825至0.851和0.857至0.904之间。这些结果表明，开发的模型在估算拖拉机AT方面是可靠的，而基于ANN的模型表现优于基于MLR的模型。强调本研究作为一种使用基于相对廉价传感器的ANN的简单方法，可以提供有用的结果，该方法可以替代现有的复杂拖拉机AT测量方法。
