TY - GEN
T1 - Research on Environmental Regulation in Facility Agriculture Based on LSTM–LLM Cooperative Mechanism
AU - Li, Wenhui
AU - Lu, Guiping
AU - Hu, Weidong
AU - Gao, Yuan
AU - Guo, Xuqi
AU - Jin, Yuhua
AU - Zhu, Meiran
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.
PY - 2026
Y1 - 2026
N2 - Amid growing global population and climate challenges, traditional agricultural models—with low resource efficiency and high environmental cost—fall short of meeting precision and sustainability goals. This study proposes an intelligent control architecture that integrates edge semantic computing and deep learning within a heterogeneous end–edge–cloud framework. The system utilizes an STM32H7 microcontroller for real-time data acquisition, deploys an LSTM-GRU model via TensorFlow Lite for edge-side time-series prediction, and enhances cloud-level decisions using a fine-tuned Large Language Model (LLM) with agricultural domain knowledge. A hybrid control strategy combining a PID-based kernel, LSTM predictive compensation, and LLM-driven dynamic correction significantly improves greenhouse regulation accuracy (MSE = 2.746). Experimental results show that, compared to a traditional PID system, the proposed LSTM-LLM scheme increases tomato growth rate by 51.3% and boosts decision frequency by 12-fold, validating its efficiency and practicality in smart agriculture.
AB - Amid growing global population and climate challenges, traditional agricultural models—with low resource efficiency and high environmental cost—fall short of meeting precision and sustainability goals. This study proposes an intelligent control architecture that integrates edge semantic computing and deep learning within a heterogeneous end–edge–cloud framework. The system utilizes an STM32H7 microcontroller for real-time data acquisition, deploys an LSTM-GRU model via TensorFlow Lite for edge-side time-series prediction, and enhances cloud-level decisions using a fine-tuned Large Language Model (LLM) with agricultural domain knowledge. A hybrid control strategy combining a PID-based kernel, LSTM predictive compensation, and LLM-driven dynamic correction significantly improves greenhouse regulation accuracy (MSE = 2.746). Experimental results show that, compared to a traditional PID system, the proposed LSTM-LLM scheme increases tomato growth rate by 51.3% and boosts decision frequency by 12-fold, validating its efficiency and practicality in smart agriculture.
KW - Edge computing
KW - Greenhouse environment control
KW - Hybrid control strategy
KW - LLM
KW - LSTM
UR - https://www.scopus.com/pages/publications/105045602046
U2 - 10.1007/978-981-95-6739-3_8
DO - 10.1007/978-981-95-6739-3_8
M3 - Conference contribution
AN - SCOPUS:105045602046
SN - 9789819567386
T3 - Communications in Computer and Information Science
SP - 115
EP - 125
BT - Advanced Computational Intelligence and Intelligent Informatics - 9th International Workshop, IWACIII 2025, Proceedings
A2 - Ma, Hongbin
A2 - Xin, Bin
A2 - She, Jinhua
A2 - Lu, Guiping
PB - Springer Science and Business Media Deutschland GmbH
T2 - 9th International Workshop on Advanced Computational Intelligence and Intelligent Informatics, IWACIII 2025
Y2 - 31 October 2025 through 4 November 2025
ER -