Evaluating Time-Series Prediction of Temperature, Relative Humidity, and CO₂ in the Greenhouse with Transformer-Based and RNN-Based Models

Abstract

In greenhouses, plant growth is directly influenced by internal environmental conditions, and therefore requires continuous management and proper environmental control. Inadequate environmental conditions make plants vulnerable to pests and diseases, lower yields, and cause impaired growth and development. Previous studies have explored the combination of greenhouse actuator control history with internal and external environmental data to enhance prediction accuracy, using deep learning-based models such as RNNs and LSTMs. In recent years, transformer-based models and RNN-based models have shown good performance in various domains. However, their applications for time-series forecasting in a greenhouse environment remain unexplored. Therefore, the objective of this study was to evaluate the prediction performance of temperature, relative humidity (RH), and CO2 concentration in a greenhouse after 1 and 3 h, using a transformer-based model (Autoformer), variants of two RNN models (LSTM and SegRNN), and a simple linear model (DLinear). The performance of these four models was compared to assess whether the latest state-of-the-art (SOTA) models, Autoformer and SegRNN, are as effective as DLinear and LSTM in predicting greenhouse environments. The analysis was based on four external climate data samples, three internal data samples, and six actuator data samples. Overall, DLinear and SegRNN consistently outperformed Autoformer and LSTM. Both DLinear and SegRNN performed well in general, but were not as strong in predicting CO2 concentration. SegRNN outperformed DLinear in CO2 predictions, while showing similar performance in temperature and RH prediction. The results of this study do not provide a definitive conclusion that transformer-based models, such as Autoformer, are inferior to linear-based models like DLinear or certain RNN-based models like SegRNN in predicting time series for greenhouse environments.

Keywords

• Autoformer
• DLinear
• LSTM
• SegRNN
• greenhouse
• time series