An EnKF-LSTM Assimilation Algorithm for Crop Growth Model
Siqi Zhou Ling Wang Jie Liu Jinshan Tang
Accurate and timely prediction of crop growth is of great significance to
ensure crop yields and researchers have developed several crop models for the
prediction of crop growth. However, there are large difference between the
simulation results obtained by the crop models and the actual results, thus in
this paper, we proposed to combine the simulation results with the collected
crop data for data assimilation so that the accuracy of prediction will be
improved. In this paper, an EnKF-LSTM data assimilation method for various
crops is proposed by combining ensemble Kalman filter and LSTM neural network,
which effectively avoids the overfitting problem of existing data assimilation
methods and eliminates the uncertainty of the measured data. The verification
of the proposed EnKF-LSTM method and the comparison of the proposed method with
other data assimilation methods were performed using datasets collected by
sensor equipment deployed on a farm.