Data-Driven Greenhouse Climate Regulation in Lettuce Cultivation Using BiLSTM and GRU Predictive Control

TL;DR

This paper presents a novel data-driven predictive control framework using LSTM and GRU neural networks for efficient greenhouse climate regulation in lettuce cultivation, reducing energy use and improving crop conditions.

Contribution

It introduces a new neural network-based MPC approach with validated robustness and real-time performance for greenhouse climate management.

Findings

GRU-based controller reduced humidity violations from 54.77% to 15.45%.

Both LSTM and GRU controllers outperformed conventional MPC.

GRU controller achieved up to 40% lower computation time.

Abstract

Efficient greenhouse management is essential for sustainable food production, but the high energy demand for climate regulation poses significant economic and environmental challenges. While traditional process-based greenhouse models exist, they are often too complex or imprecise for reliable control. To address this, our study introduces a novel data-driven predictive control framework using Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks within a Model Predictive Control (MPC) architecture. Training data were generated from a validated dynamic model simulating lettuce cultivation under various environmental conditions. The LSTM and GRU networks were trained to predict future greenhouse states -- including temperature, humidity, CO\textsubscript{2} concentration, and crop dry matter -- with robustness confirmed via $10$-fold cross-validation. These networks were embedded into an online MPC controller to optimize heating, ventilation, and CO\textsubscript{2} injection, aiming to minimize energy consumption and maximize crop yield while respecting biological constraints. Results showed that both the LSTM- and GRU-based controllers significantly outperformed a conventional MPC baseline. For example, humidity violations dropped from 54.77\% (MPC) to 15.45\% (GRU) and 17.71\% (LSTM), while day-night temperature deviations were kept below $2^\circ\text{C}$. The GRU controller further achieved up to 40\% lower computation time than its LSTM counterpart, confirming its real-time feasibility. Overall, the proposed GRU-driven predictive control approach offers a robust and computationally efficient solution for intelligent greenhouse climate automation under practical operational constraints.