现代电子技术2025，Vol.48Issue(19)：99-102,4.DOI:10.16652/j.issn.1004-373x.2025.19.016
基于长短期记忆网络的粮食产量趋势预测方法
Grain yield trend prediction method based on long short-term memory networks
郭艳辉 1王伟1
作者信息
- 1. 河南师范大学 计算机与信息工程学院,河南 新乡 453007
- 折叠
摘要
Abstract
A grain yield trend prediction method based on long short-term memory networks is studied to accurately capture the relationship between different factors and grain yield trends,and provide more reliable and effective support for agricultural production.In the course of processing the raw grain yield sample data with the WT-EEMD method,the high and low frequency components are decomposed and obtained by wavelet transform(WT).The modal components are obtained by ensemble empirical mode decomposition(EEMD).The trend characteristics of grain yield changes are captured by concatenating the decomposition results.The key features that affect grain yield are screened with a feature selection method based on dynamic correlation.The extracted feature set and historical grain yield time series are input into an LSTM networks based yield prediction model.The model parameters are optimized with particle swarm optimization(PSO)algorithm.After learning the long-term impact of feature vectors on grain yield,the grain yield prediction results are output.The experimental results show that the method can achieve grain yield trend prediction with a prediction error of no more than 1.63%.After feature extraction and selection,the value of R2 for predicting grain yield can reach 0.93,with a mean absolute error(MAE)of 0.41,which verifies the validity of the proposed method.关键词
粮食产量/趋势预测/WT-EEMD/高低频分量/模态分量/动态相关性/特征集/产量预测模型Key words
grain yield/trend prediction/WT-EEMD method/high and low frequency component/modal component/dynamic correlation/feature set/production prediction model分类
信息技术与安全科学引用本文复制引用
郭艳辉,王伟..基于长短期记忆网络的粮食产量趋势预测方法[J].现代电子技术,2025,48(19):99-102,4.
