计算机技术与发展2024，Vol.34Issue(5)：183-189,7.DOI:10.20165/j.cnki.ISSN1673-629X.2024.0058
基于ME-BiLSTM模型的苜蓿叶面积指数预测方法
Prediction Method of Alfalfa Leaf Area Index Based on ME-BiLSTM Model
摘要
Abstract
The continuous temporal Leaf Area Index(LAI)reflects the changes in alfalfa growth.Predicting future LAI in alfalfa plays a crucial role in guiding field management decisions.Aiming at the problem of insufficient training data for alfalfa temporal LAI due to dif-ficulties in LAI data collection,we employ the growth days as independent variables and utilize a modified Logistic model to dynamically model the observed changes in alfalfa LAI.By interpolating data based on the simulated LAI curve,a three-year daily alfalfa LAI dataset for the Ningxia Yellow River Irrigation District experimental area is constructed.To address abrupt data changes after alfalfa cutting,we introduce the ME-BiLSTM model which integrates the Moving Sum and Moving Average(MOSUM)method with a Bidirectional Long Short-Term Memory(BiLSTM)encoder-decoder neural network.The MOSUM detects mutation points in the LAI dataset and eliminates training batches containing these points,followed by predictions using the improved BiLSTM model.It is demonstrated that the ME-BiLSTM model predicts future alfalfa LAI curve changes effectively,with coefficient of determination(R2)and root mean square error(RMSE)values of 0.998 5 and 0.072 2,respectively.The first and fourth alfalfa growth cycles have the best predictive model ac-curacy,whereas the second and third cycles have slightly lower accuracy.关键词
苜蓿/叶面积指数/Logistic模型/MOSUM/双向长短期记忆网络Key words
alfalfa/leaf area index/Logistic model/MOSUM/bidirectional long short-term memory network分类
信息技术与安全科学引用本文复制引用
杨松涛,葛永琪,王静,刘瑞..基于ME-BiLSTM模型的苜蓿叶面积指数预测方法[J].计算机技术与发展,2024,34(5):183-189,7.基金项目
国家自然科学基金地区科学基金项目(62262052,62162052) （62262052,62162052）
宁夏回族自治区重点研发计划(2021BEB04016,2022BDE03007) （2021BEB04016,2022BDE03007）
宁夏自然科学基金(2021AAC03041,2022AAC03004) （2021AAC03041,2022AAC03004）
