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Performance Analysis and Modelling of Impact-based Sensor in Yield Monitor System

机译:基于冲击的传感器在产量监测系统中的性能分析与建模

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In order to accurately acquire the spatial distribution of grain yield, the impact-based yield monitor system of grain combine harvester was independently developed. The yield monitor system consists of a flow sensor module, a data acquisition module, a GNSS module, and a yield management terminal. In this paper, the designed system was used for collecting yield data. The yield prediction model for different areas was established by the voltage, then it was applied to predict regional yield. The universality of model was analyzed. Based on the characteristics of spatial field variability, the preprocessing method of threshold filtering and the local average interpolation was used before establishing the relational model between actual yield and voltage. Field experiments included data acquisition part and model establishment part. The Data acquisition experiments were carried out in two fields, which were respectively defined as F1 and F2. The experiment in F1 were repeat 3 times, which were represented as group A1~A3. The experiment in F2 were repeat 6 times, which were B1~B6. The relational model was established between weight and voltage of each group. The mutual prediction verification was performed to demonstrate model universality. As a result, F2 yield was predicted by predictive model of F1 indicated that the relative error was 20.06%, which were not universal. The intra-group prediction results of Fl showed that the lowest relative error was 6.36%, the accuracy of the model need to improve furtherly. When B1 and B2 groups were mutually modeling and verification in the F2, the relative error of predicted yield was less than 5%. Modeling and verification accuracy R~2 were both above 0.9, which proved predictive models of B1 and B2 were highly accurate. However, it was not suitable for other groups forecast. The same result also appeared in B5 and B6 in the F2. The results showed that the system can correctly judge the grain yield changes. The plot of yield map in the plane-coordinate system can provide reference for fine farming and harvesting in the next quarter.
机译:为了准确获取籽粒产量的空间分布,独立开发了基于谷物结合收割机的基于影响的产量监测系统。产量监测系统包括流量传感器模块,数据采集模块,GNSS模块和产量管理终端。在本文中,设计系统用于收集产量数据。由电压建立不同区域的产量预测模型,然后应用其以预测区域产量。分析了模型的普遍性。基于空间场变异性的特点,在实际产量和电压之间建立关系模型之前使用了阈值滤波的预处理方法和局部平均插值。现场实验包括数据采集部分和模型建立部分。数据采集​​实验在两个场中进行,分别定义为F1和F2。 F1的实验重复3次,其表示为A1〜A3组。 F2的实验重复6次,即B1〜B6。关系模型是在每个组的重量和电压之间建立的。互相预测验证进行了展示模型普遍性。结果,通过F1的预测模型预测F2产量,表明相对误差为20.06%,这不是普遍的。 FL的组内预测结果表明,最低的相对误差为6.36%,模型的准确性需要进一步改进。当B1和B2组在F2中相互建模和验证时,预测产量的相对误差小于5%。建模和验证精度R〜2均高于0.9,证明了B1和B2的预测模型非常准确。但是,它不适合其他群体预测。相同的结果也出现在F2中的B5和B6中。结果表明,该系统可以正确判断谷物产量的变化。平面坐标系中的产量图的曲线可以参考下一季度的精细农业和收获。

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