Multi-model wheat seedling growth cabin optimal parameter prediction method based on Kalman filter

The invention discloses a multi-model wheat seedling growth cabin optimal parameter prediction method based on a Kalman filter, and belongs to the field of intelligent equipment optimization. The Kalman filter is adopted to filter data collected by the sensor in the growth cabin system, and a value obtained through Kalman filtering is closer to a true value than a value directly collected by the sensor, so that accurate control of temperature, humidity and CO2 concentration in the growth cabin system is facilitated. Then, wheat seedling growth is taken as multiple influence factors, NaCl concentration, an illumination-dark ratio, an illumination period and seed weight are added, and are respectively input into a multivariate nonlinear regression model, a radial basis function neural network model and a multi-layer perceptron neural network model for prediction. A better model structure is selected, optimal environment parameters of wheat seedling growth are found more accurately, and a certain reference is pr