[1]卢宏宇,贡宇,任妮,等.基于DBO-LSTM-Attention模型的设施番茄茎粗预测[J].江苏农业学报,2026,42(05):982-989.[doi:doi:10.3969/j.issn.1000-4440.2026.05.012]
　LU Hongyu,GONG Yu,REN Ni,et al.Prediction of stem diameter of protected tomato based on DBO-LSTM-Attention model[J].,2026,42(05):982-989.[doi:doi:10.3969/j.issn.1000-4440.2026.05.012]
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基于DBO-LSTM-Attention模型的设施番茄茎粗预测()

江苏农业学报[ISSN:1006-6977/CN:61-1281/TN]

卷:
42
期数:
2026年05期
页码:
982-989
栏目:
农业信息工程
出版日期:
2026-05-31

文章信息/Info

Title:
Prediction of stem diameter of protected tomato based on DBO-LSTM-Attention model
作者:
卢宏宇1; 2; 贡宇2; 任妮1; 2; 金晶2; 李德翠2; 刘磊1; 毛晓娟2
(1.淮安大学计算机与软件工程学院,江苏淮安223001；2.江苏省农业科学院农业信息研究所/农业农村部长三角智慧农业技术重点实验室,江苏南京210014)
Author(s):
LU Hongyu1; 2; GONG Yu2; REN Ni1; 2; JIN Jing2; LI Decui2; LIU Lei1; MAO Xiaojuan2
(1.School of Computer and Software Engineering, Huai’an University, Huai’an 223001, China;2.Institute of Agricultural Information, Jiangsu Academy of Agricultural Sciences/Key Laboratory of Intelligent Agricultural Technology (Yangtze River Delta), Ministry of Agriculture and Rural Affairs, Nanjing 210014, China)
关键词:
番茄; 茎粗; 时序预测模型; 蜣螂优化算法; 长短期记忆网络; 注意力机制
Keywords:
tomato; stem diameter; time series prediction model; dung beetle optimizer; long short-term memory network; attention mechanism
分类号:
S641.2
DOI:
doi:10.3969/j.issn.1000-4440.2026.05.012
文献标志码:
A
摘要:
茎粗变化量是评估番茄生长状况的重要生理指标,提前预测其动态变化对温室环境的精准调控具有重要意义。针对现有模型在茎粗预测中存在特征提取不充分、长时间依赖关系捕捉能力不足等问题,本研究提出一种融合蜣螂优化算法(DBO)、长短期记忆网络(LSTM)与注意力机制(Attention)的茎粗预测模型(DBO-LSTM-Attention)。该模型利用LSTM捕捉茎粗变化量与空气温度、空气湿度、二氧化碳浓度、光合有效辐射等环境因子之间的时间依赖关系；引入注意力机制动态分配权重,增强模型对关键时间步的关注能力；采用DBO算法实现超参数自适应寻优,提升模型泛化性能。结果表明,在短时长和长时长预测任务中,DBO-LSTM-Attention模型均表现出较高的预测稳定性与准确性,各项评价指标均优于对比模型。且随着预测时长增加,模型预测性能下降幅度较小,表明其具有较强的时序建模与泛化能力。综上,DBO-LSTM-Attention模型能够有效融合番茄植株生长参数与温室环境因子,实现对茎粗动态变化的高精度预测,为设施番茄生长环境的智能调控提供了理论依据。
Abstract:
Stem diameter variation is a vital physiological indicator for evaluating tomato growth status. Accurately predicting its dynamic changes in advance is of great significance for precise regulation of greenhouse environments. Aiming at the deficiencies of existing prediction models such as insufficient feature extraction and weak capability in capturing long-term dependencies, this study proposed a stem diameter prediction model integrating dung beetle optimizer (DBO), long short-term memory (LSTM) network and attention mechanism, namely DBO-LSTM-Attention. The model used LSTM module to capture temporal dependencies between stem diameter dynamics and environmental factors including air temperature, air humidity, carbon dioxide concentration and photosynthetically active radiation. The attention mechanism was introduced to dynamically assign weights, thereby enhancing the model’s focus on critical time steps. The DBO algorithm was adopted for adaptive hyperparameter optimization to improve model generalization perfor-mance. The results revealed that the DBO-LSTM-Attention model achieved superior prediction stability and accuracy in both short-term and long-term prediction tasks, with all evaluation indices outperforming those of the comparative models. Its performance declined slightly with the increase of prediction horizon, proving strong temporal modeling and generalization ability. In conclusion, the DBO-LSTM-Attention model can effectively fuse tomato plant growth parameters and greenhouse environmental factors to realize high-precision prediction of dynamic changes in stem diameter, and provide a theoretical reference for intelligent regulation of growing environments for protected tomatoes.

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备注/Memo

备注/Memo:
收稿日期：2026-03-24基金项目：农业农村部科技项目作者简介：卢宏宇(1997-),男,重庆人,硕士研究生,研究方向为设施环境智能调控。(E-mail)rogerluhongyu@163.com通讯作者：毛晓娟,(E-mail)20190033@jaas.ac.cn
更新日期/Last Update: 2026-06-17