A recently published 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, analyzing 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 modeling techniques are then discussed.
Compared to Jogunola et al. (see study)., the RMSE for the SVR simulation process is significantly higher (1.76 °C vs. 0.025 °C). Due to the nature of the CNN and LSTM learning process, these models are able to learn sequences of time-dependent events better than the almost linear SVR process. This leads to better predictive capabilities and much more accurate modeling. However, the resolution of the data in Jogunola et al. [38] was hourly using either all or only one input feature. The disparity in RMSE is then justified as a higher resolution can introduce more errors into the model, especially when a prediction is fed back into the model to predict the following time. Further, the major trade-off in this accuracy is computation time for the neural network models compared to the simplistic nature of the SVR. For the analytical model, its RMSE is also significantly higher than that seen in Jogunola et al., but this, too, is a much simpler and quicker modeling technique that requires a one-off parameter optimization for the different environmental variables.
When focusing on the analytical model, it is clear that it is accurate when compared to other literature, in particular, Nauta et al. (see study), who achieved an RMSE of 4.25 °C. In Tong et al. (see study), the authors used far more sensors and more cumbersome thermodynamic and heat transfer equations to produce an error of 1.0 °C at night and 1.5 °C during the day.
Therefore, it is clear that although the models are not as accurate as the models developed in the literature, they have a low computational cost and simpler implementation of modeling techniques that can lead to similar results and a higher resolution of predictions that can aid in decision making in near real-time. Having the capability to forecast tunnel temperatures one hour in advance allows farmers to enhance their readiness for unforeseen temperature changes. This, in turn, enables farmers to make more informed decisions regarding crop management within greenhouse tunnels. Additionally, this advancement contributes to an enhanced comprehension of thermodynamics within South African greenhouse tunnels, paving the way for improved physics-based modeling of African greenhouses in the times to come.
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Modeling the Temperature Inside a Greenhouse Tunnel
by Keegan Hull, Pieter Daniel van Schalkwyk, Mosima Mabitsela, Ethel Emmarantia Phiri, and Marthinus Johannes Booysen