Differentiable Physics-based Greenhouse Simulation

TL;DR

This paper introduces a differentiable, physics-based greenhouse simulation model that accurately predicts climate and crop dynamics over long periods by training on real data, enhancing simulation precision.

Contribution

It presents a novel interpretable simulation model based on linear differential equations that can be trained efficiently from real greenhouse data.

Findings

Model accurately predicts cucumber growth in greenhouse.

Training significantly improves simulation accuracy.

Effective handling of missing data states.

Abstract

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.