  Greenhouse production systems play a crucial role in modern agriculture,
enabling year-round cultivation of crops by providing a controlled environment.
However, effectively quantifying uncertainty in modeling greenhouse systems
remains a challenging task. In this paper, we apply a novel approach based on
sparse Bayesian deep learning for the system identification of lettuce
greenhouse models. The method leverages the power of deep neural networks while
incorporating Bayesian inference to quantify the uncertainty in the weights of
a Neural ODE. The simulation results show that the generated model can capture
the intrinsic nonlinear behavior of the greenhouse system with probabilistic
estimates of environmental variables and lettuce growth within the greenhouse.
