Prediction of rice yield with environmental factors based on deep learning technology
摘要
Rice is a globally important staple crop, and the accurate prediction of rice yield plays a significant role in agricultural development. Due to the influence of external environmental factors and the growth mechanisms of rice, rice yield often exhibits nonlinear characteristics, making it challenging to make precise predictions. Therefore, the CE-CGRU rice yield prediction model is proposed, which extracts features using the Copula Entropy (CE) method for nonlinear environmental factors and combines them with CNN and GRU technologies. The aim is to identify crucial features for yield prediction under specific rice varieties.Based on the analysis and performance comparison using real data from Lin'an District of Zhejiang Province, the proposed model is compared to five other yield prediction models: MLR, RF, LSTM, GRU, and CNN-LSTM. The results indicate that the CE-CGRU model achieves a MAE of 0.677, a MSE of 0.87, and a MAPE of 5.029%, demonstrating its superior capability in capturing the complex nonlinear relationship between rice yield and environmental factors. Furthermore, a comparison and analysis of different feature selection methods and time steps are conducted.