Sustainable greenhouse microclimate modeling: a comparative analysis of recurrent and Graph Neural Networks

The integration of photovoltaic (PV) systems into greenhouses not only optimizes land use but also enhances sustainable agricultural practices by enabling dual benefits of food production and renewable energy generation. However, accurate prediction of internal temperature is crucial to ensure optimal crop growth while maximizing energy production. This study introduces a novel application of Spatio-Temporal Graph Neural Networks (STGNNs) to greenhouse microclimate modeling, comparing their performance with traditional Recurrent Neural Networks (RNNs). While RNNs excel at temporal pattern recognition, they cannot explicitly model directional relationships between environmental variables. Our STGNN approach addresses this limitation by representing these relationships as directed graphs, enabling the model to capture both environmental dependencies and their directionality. We benchmark RNNs against directed STGNNs on two 15-min resolution datasets from Volos (Greece): a four-variable, driver-response campaign (2020) and an eight-variable, feedback-rich campaign (2024) that adds PAR and CO2. In the 2020 case the RNN attains near-perfect accuracy, outperforming the STGNN. When additional drivers are available in 2024, the STGNN overtakes the RNN (R2 = 0.905 vs 0.740), demonstrating that explicitly modeling directional dependencies becomes critical as interaction complexity grows. These findings indicate that graph-based models become worthwhile when new sensors create cross-links that a sequence model cannot untangle. This insight helps practitioners balance model complexity against instrumentation cost and supplies a fast empirical core for real-time digital twins that jointly optimize crop yield and PV yield in agrivoltaic houses.