基于卡尔曼滤波器和多层感知器的大麦幼苗最优生长参数预测

Prediction of optimal growth parameters of barley seedling based on Kalman filter and multilayer perceptron

为了提高生长舱大麦幼苗的质量和种植效率,首先利用卡尔曼滤波算法对传感器采集的数据进行处理,有效降低了环境因素和传感器本身误差的影响,提高了采集数据的精度,保证生长舱的精确控制和准确的实验数据,然后利用多元非线性回归、径向基函数和多层感知器神经网络对不同条件下,大麦种子萌发生长约160 h后的平均生长高度、麦苗重量和种子重量干燥比进行分析比较,结果表明,多层感知器网络模型对数据的拟合效果最好。利用该模型预测最优环境时的大麦幼苗平均高度和麦苗种子重量比与实际种植效果基本一致,为生长舱大麦幼苗的种植提供一定参考。

In order to improve the quality and planting efficiency of barley seedlings in the growth chamber,the Kalman filter algorithm was firstly used to process the data collected by the sensor,which effectively reduced the influence of environmental factors and the error of the sensor itself,improved the accuracy of the collected data,and ensured the precise control in the growth chamber and accurate test data.Then multiple nonlinear regression,radial basis function and multilayer perceptron neural network were used to analyze the average growth height,seedling weight and seed weight of barley seeds about 160 hours after germination under different conditions.The drying ratio was analyzed and compared.The results show that the multi-layer perceptron network model fits the data best.Using this model to predict the average height of barley seedlings and the ratio of seedling weight of barley seedlings in the optimal environment is basically consistent with the actual planting effect,which provides a certain reference for the planting of barley seedlings in the growth chamber.