Greenhouse environment modeling and simulation for microclimate control

Greenhouse plant science assays have been impacted by microclimates which causes significant level of noise to plant growth measurement data. Researchers and scientist have been randomizing pots locations, which helps to re-distribute the noise, but does not remove the noise. The impacts from microclimates can be eliminated by shuffling plants, but there has been no study on the optimization of shuffling pattern, such as the frequency and moving distance for the pots. It is important to quantitatively study the microclimates in the greenhouse, so we can optimize the shuffling pattern accordingly. The aim of this study was to propose a computer modeling approach for simulating microclimate in the greenhouse, and then use the simulation result to optimize pot movement distance and frequency. A computational greenhouse model was developed using inputs from real design, materials and location of a Purdue Lily greenhouse in West Lafayette, Indiana. Microclimate variables, including ambient temperature and lighting radiation over 24 h and 7 days were predicted with the simulation model. Thermometers and lighting sensors were also distributed in the greenhouse for the ground-truth measurements over a seven-day period. Comparison of measured microclimate variables with predicted variables obtained from the computational model demonstrated that the simulation model could precisely predict temperatures and light radiation at any time, and at different positions in the greenhouse. Optimized pot movement frequency and distance were then determined with the simulation result. The new shuffling pattern can remove over 90% of the microclimate variance but could save more than 95% shuffling efforts compared with non-stop movement.