Forecasting greenhouse air and soil temperatures: A multi-step time series approach employing attention-based LSTM network

Greenhouses stand as key infrastructural components in contemporary agriculture, facilitating the perennial availability of vegetables. Harnessing accurate, real-time environmental data, particularly air and soil temperature, is pivotal to promoting the facility's efficiency in pest and disease alarming and vegetable production. Nevertheless, despite consistent long-term temperature trends, short-term fluctuations prove to be considerably nonlinear, time-lagged, and multivariate-coupled due to the unique microclimate of the greenhouse. Addressing this, we propose a novel short-term multi-step prediction model that assimilates a range of environmental data including air temperature, humidity, soil temperature, and soil moisture content at diverse greenhouse heights. This model utilizes an Attention-LSTM based time series approach to accurately predict multi-step short-term temperature variations within the greenhouse. By employing a modest amount of historical data (approximately 48 h), the model provides future temperature forecasts within a range of 30 to 480 min with high accuracy. When benchmarked against renowned models such as RNN, GRU, and LSTM networks, our proposed models display impressive performance. It achieved R2 values of 0.93, 0.94, 0.95, and 0.86 for 1, 4, 8, and 16-step predictions, respectively, in the domain of air temperature forecasting. The corresponding MSE values stood at 0.42, 0.32, 0.27, and 0.81. In parallel, for soil temperature forecasts, our model recorded R2 values of 0.96, 0.94, 0.92, and 0.89 for 1, 4, 8, and 16-step predictions, respectively, alongside MSE values of 0.36, 0.48, 0.73, and 0.99 for the equivalent steps. To conclude, these results reveal the exceptional predictive aptitude of our model for both air and soil temperature forecasts within greenhouse environments, validating its standing as an effective tool for the optimization of vegetable farming practices. Consequently, our model demonstrates its ability to yield precise predictions of environmental variables within greenhouses, offering crucial early warning mechanisms for the enhancement of vegetable cultivation processes.