  We present a differentiable greenhouse simulation model based on physical
processes whose parameters can be obtained by training from real data. The
physics-based simulation model is fully interpretable and is able to do state
prediction for both climate and crop dynamics in the greenhouse over very a
long time horizon. The model works by constructing a system of linear
differential equations and solving them to obtain the next state. We propose a
procedure to solve the differential equations, handle the problem of missing
unobservable states in the data, and train the model efficiently. Our
experiment shows the procedure is effective. The model improves significantly
after training and can simulate a greenhouse that grows cucumbers accurately.
