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Sensor Fusion with NARX Neural Network to Predict the Mass Flow in a Sugarcane Harvester

机译：传感器融合与NARX神经网络预测甘蔗收割机中的质量流量

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摘要

Measuring the mass flow of sugarcane in real-time is essential for harvester automation and crop monitoring. Data integration from multiple sensors should be an alternative to receive more reliable, accurate, and valuable predictions than data delivered by a single sensor. In this sense, the objective was to evaluate if the fusion of different sensors installed in a sugarcane harvester improves the mass flow prediction accuracy. A harvester was experimentally instrumented, and neural network models integrated sensor data along the harvester to perform the self-calibration of these sensors and estimate the mass flow. Nonlinear autoregressive networks with exogenous input (NARX) and multiple linear regression (MLR) models were compared to predict the mass flow. The prediction with the NARX showed a significant superiority over MLR. MLR decreases the estimated mass flow variability in the harvester. NARX with multi-sensor data has an RMSE of 0.3 kg s−1, representing a MAPE of 0.7%. The fusion of sensor signals improves prediction accuracy, with higher performance than studies with approaches that used a single sensor. The mass flow approach with multiple sensors is a potential approach to replace conventional yield monitors. The system generates accurate data with high sample density within sugarcane rows.
机译：测量实时甘蔗的质量流动对于收割机自动化和作物监测是必不可少的。来自多个传感器的数据集成应该是接收比单个传感器传送的数据更可靠，准确，有价值的预测的替代方案。从这个意义上讲，目的是评估在甘蔗收割机中安装的不同传感器的融合是否提高了质量流量预测精度。一个收割机在实验仪器中，神经网络模型沿着收割机集成了传感器数据，以执行这些传感器的自校准并估计质量流量。将非线性自回归网络与外源输入（NARX）和多元线性回归（MLR）模型进行比较，以预测质量流量。与NARX的预测显示在MLR上具有显着的优越性。 MLR降低收割机中的估计质量流动变异性。具有多传感器数据的NARX具有0.3kg S-1的RMSE，表示为0.7％的mape。传感器信号的融合提高了预测精度，而性能比使用单个传感器的方法更高。具有多个传感器的质量流量方法是替代常规收益监视器的潜在方法。该系统在甘蔗排中产生具有高样本密度的准确数据。

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