江苏农业学报2026，Vol.42Issue(5)：982-989,8.DOI:10.3969/j.issn.1000-4440.2026.05.012
基于DBO-LSTM-Attention模型的设施番茄茎粗预测
Prediction of stem diameter of protected tomato based on DBO-LSTM-Attention model
摘要
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 mod-ule 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 hyperparame-ter 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.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Key words
tomato/stem diameter/time series prediction model/dung beetle optimizer/long short-term memory network/attention mechanism分类
农业科技引用本文复制引用
卢宏宇,贡宇,任妮,金晶,李德翠,刘磊,毛晓娟..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(5):982-989,8.基金项目
农业农村部科技项目 （）
