灌溉排水学报2025，Vol.44Issue(5)：122-132,11.DOI:10.13522/j.cnki.ggps.2024406
基于HMM+LSTM算法的网纹蜜瓜数字孪生体生长模型设计
Design of netted muskmelon digital twin growth model based on HMM+LSTM algorithm
摘要
Abstract
[Objective]The purpose of this paper is to enhance agricultural water use efficiency,and to develop a digital twin system for simulating the entire growth life-cycle of crops,which holds significant importance for advancing smart agriculture in China and assisting farmers in formulating optimized management strategies.[Method]Using netted muskmelon as a case,in the Yellow River Diversion Irrigation District of Huayuankou,Henan Province,we conducted controlled indoor experiments replicating local climatic conditions.An IoT-based monitoring network was employed to collect real-time data on environmental parameters and growth status throughout the cultivation process.The digital twin model was developed using 3ds Max for 3D modeling and Unity 3D for visualization,while the growth prediction model was built by integrating Hidden Markov Model(HMM)and Long Short-Term Memory(LSTM)algorithms.[Result]Simulation results demonstrated high recognition accuracy across different growth stages:85.3%for seed and seedling stages,78.6%for leaf stage,with an overall average accuracy of 82.8%.[Conclusion]The proposed system,combining wireless sensor networks with HMM+LSTM algorithms to generate 3D growth models of muskmelon digital twins,achieves precise,efficient,and non-destructive visualization of the entire growth process,and can be extended to construct digital twins for other crops.关键词
数字孪生/网纹蜜瓜/隐马尔可夫模型HMM/长短期记忆网络算法LSTM/智慧农业Key words
digital twin/netted muskmelon/Hidden Markov Model(HMM)/Long Short-Term Memory algorithm(LSTM)/smart agriculture分类
管理科学引用本文复制引用
陆棚,刘明堂,吴姗姗,李斌,李世豪,王长春,杨阳蕊,江恩慧..基于HMM+LSTM算法的网纹蜜瓜数字孪生体生长模型设计[J].灌溉排水学报,2025,44(5):122-132,11.基金项目
国家自然科学基金项目(U2243601,52409058) （U2243601,52409058）
2025年度河南省高等学校重点科研项目指导性计划项目(25B510010) （25B510010）
2025年度水利部黄河下游河道与河口治理重点实验室开放课题基金项目(2025007) （2025007）
