基于SSA-RFR算法的采棉机测产传感器研究

Yield Sensor of Cotton Picker Based on SSA - RFR Algorithm

随着棉花种植和收获的机械化程度提高,获取准确的产量图,分析田间产量数据,变得尤为重要,而采棉机作业时在输棉管道处监测产量是一种有效、可行的方法。现有光电对射式棉花测产传感器在作业中会有粘液遮挡检测通道、环境光影响等问题,面对复杂的田间作业环境,传感器标定普遍采用线性或多项式模型,精度和抗干扰性表现不够理想。针对上述现状,本文首先在传感器的结构和电路设计上做了抗干扰改进。然后在传感器标定过程中,尝试使用随机森林回归模型(Random forest regression, RFR),对实验样本进行训练、测试。在分析模型的表现后,提出了麻雀算法(Sparrow search algorithm, SSA)改进的随机森林回归模型,以均方误差作为适应度,对模型进行优化。经过验证,在相同验证集下,优化后的模型有更好的检测精确度。通过研究寻优上下界范围,平衡运行时间和检测精度,得到最优检测模型。该模型在验证集上表现良好,决定系数R~2为0.99,平均绝对百分比误差(MAPE)为6.34%。台架实验结果表明,不同风速下最大误差为9.21%,平均误差为8.33%,改进后的传感器及检测模型性能良好,能够较准确检测采棉机作业时棉花产量。

With the increase of mechanization of cotton planting and harvesting,it is particularly important to obtain accurate yield map and analyze field yield data,and it is an effective and feasible method to monitor the yield at the cotton conveying pipeline during the operation of cotton picker.The existing photoelectric beam cotton yield measurement sensor has problems such as mucus blocking detection channel and ambient light influence in operation.Facing the complex field working environment,linear or polynomial model is generally used for sensor calibration,and the accuracy and anti-interference performance are not ideal.In view of the above situation,the anti-interference in the structure and circuit design of the sensor was firstly improved.Then,in the process of sensor calibration,random forest regression(RFR)was used to train and test the experimental samples.After analyzing the performance of the model,a stochastic forest regression model based on sparrow search algorithm(SSA)was proposed.The mean square error was used as fitness value to optimize the model.After verification,the optimized model had better detection accuracy under the same verification set.The optimal detection model was obtained by optimizing the range of upper and lower bounds,balancing the running time and detection accuracy.The model performed well on the validation set with a coefficient of determination(R2)of 0.99 and a mean absolute percentage error(MAPE)of 6.34%.The bench test results showed that the maximum error was 9.21%and the average error was 8.33%at different wind speeds.The improved sensor and detection model had good performance and can accurately detect the cotton quality during the operation of the cotton picker.