Modelling the Temperature Inside a Greenhouse Tunnel

Abstract

Climate-change-induced unpredictable weather patterns are adversely affecting global agricultural productivity, posing a significant threat to sustainability and food security, particularly in developing regions. Wealthier nations can invest substantially in measures to mitigate climate change’s impact on food production, but economically disadvantaged countries face challenges due to limited resources and heightened susceptibility to climate change. To enhance climate resilience in agriculture, technological solutions such as the Internet of Things (IoT) are being explored. This paper introduces a digital twin as a technological solution for monitoring and controlling temperatures in a greenhouse tunnel situated in Stellenbosch, South Africa. The study incorporates an aeroponics trial within the tunnel, analysing temperature variations caused by the fan and wet wall temperature regulatory systems. The research develops an analytical model and employs a support vector regression algorithm as an empirical model, successfully achieving accurate predictions. The analytical model demonstrated a root mean square error (RMSE) of 2.93 °C and an R2 value of 0.8, while the empirical model outperformed it with an RMSE of 1.76 °C and an R2 value of 0.9 for a one-hour-ahead simulation. Potential applications and future work using these modelling techniques are then discussed.

Keywords

• greenhouse tunnel
• data-driven thermal model
• analytic thermal model
• digital twin
• Internet of Things
• smart agriculture