中圖分類號：TP18 文獻(xiàn)標(biāo)志碼：A DOI: 10.16157/j.issn.0258-7998.234657 中文引用格式： 張春磊，李顏娥，丁煜，等. 基于深度學(xué)習(xí)技術(shù)的水稻環(huán)境因素產(chǎn)量預(yù)測[J]. 電子技術(shù)應(yīng)用，2024，50(4)：81-86. 英文引用格式： Zhang Chunlei，Li Yan′e，Ding Yu，et al. Prediction of rice yield with environmental factors based on deep learning technology[J]. Application of Electronic Technique，2024，50(4)：81-86.
Prediction of rice yield with environmental factors based on deep learning technology
1.College of Mathematics and Computer Science， Zhejiang A&F University； 2.Key Laboratory of Forestry Intelligent Monitoring and Information Technology of Zhejiang Province； 3.China Key Laboratory of State Forestry and Grassland Administration on Forestry Sensing Technology and Intelligent Equipment； 4.Hangzhou Lin'an District Agricultural and Rural Information Service Center
Abstract： 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.
Key words : rice yield prediction；Copula Entropy；deep learning；CE-CGRU
