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

Abstract

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.

Keywords

• barley seedling germination;Kalman filter;radial basis function;multilayer perceptron;sensor