使用机器学习算法对子 (Corylus avellana L.) 的终端速度和拖动系数进行非破坏性估计,基于一些水果的物理性质
Onder Kabas1, Mehmet Kayakus2, Georgiana Moiceanu3
1Department of Machine, Technical Science Vocational School, Akdeniz University, Antalya 07070, Türkiye.
Foods (Basel, Switzerland)
|August 12, 2023
概括
这项研究使用机器学习估计子的空气动力学特性,如终端速度和阻力系数. 人工神经网络 (ANN) 为这些关键的农业工程参数提供了最准确的预测.
科学领域:
- 农业工程 农业工程
- 空气动力学 航空动力学
- 机器学习应用 机器学习应用
背景情况:
- 子的生产在全球意义重大,起源于土耳其.
- 了解子的空气动力学特性对于设计农业机械至关重要,特别是用于收获和收获后操作.
研究的目的:
- 为了估计子的终端速度和阻力系数.
- 评估机器学习模型在预测这些空气动力学特性方面的有效性.
主要方法:
- 使用后勤回归 (LR),支持向量回归 (SVR) 和人工神经网络 (ANN).
- 独立变量包括湿度,质量,密度,预测面积,表面积和几何直径.
- 模型性能使用R平方 (R2),平均平方误差 (MSE) 和平均绝对误差 (MAE) 度量来评估.
主要成果:
- 人工神经网络 (ANN) 证明了最高的准确性.
- 基于R2.2,ANN模型实现了终端速度的91.5%准确度和阻力系数的85.9%.
- SVR和LR模型也显示了成功的估计,SVR是第二个最成功的.
结论:
- 机器学习模型,特别是ANN,可以成功地估计子末端速度和阻力系数.
- 准确的空气动力学性能数据支持子加工和收获设备的改进设计和效率.
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