Core Concepts
The EnKF-LSTM algorithm combines ensemble Kalman filter and LSTM neural network to improve crop growth model predictions by handling low-quality observation data effectively.
Abstract
The EnKF-LSTM algorithm integrates ensemble Kalman filter and LSTM neural network to enhance crop growth model predictions by addressing the challenges of missing and incorrect observation data. It significantly reduces errors compared to traditional methods like EnKF, CNN, GRU, and FNN. The method is validated using sensor data from a farm in China, showing promising results for rice, maize, and soybean crops.
Stats
The MSE decreased by 96.71% in rice experiments.
RMSE decreased by 92.52% in maize experiments.
MAE decreased by 93.75% in soybean experiments.
Quotes
"The EnKF-LSTM method showed a decrease of 10.56% in MSE compared to CNN."
"The EnKF-LSTM method exhibited a decrease of 87.60% in RMSE compared to the unassimilated data."