Optimization and control of the light environment for greenhouse crop production

Optimization and control of the greenhouse light environment is key to increasing crop yield and quality. However, the light saturation point impacts the efficient use of light. Therefore, the dynamic acquisition of the light saturation point that is influenced by changes in temperature and CO₂ concentration is an important challenge for the development of greenhouse light environment control system. In view of this challenge, this paper describes a light environment optimization and control model based on a crop growth model for predicting cucumber photosynthesis. The photosynthetic rate values for different photosynthetic photon flux densities (PPFD), CO₂ concentration, and temperature conditions provided to cucumber seedlings were obtained by using an LI-6400XT portable photosynthesis system during multi-factorial experiments. Based on the measured data, photosynthetic rate predictions were determined. Next, a support vector machine(SVM) photosynthetic rate prediction model was used to obtain the light response curve under other temperatures and CO₂ conditions. The light saturation point was used to establish the light environment optimization and control model and to perform model validation. The slope of the fitting straight line comparing the measured and predicted light saturation point was 0.99, the intercept was 23.46 and the coefficient of determination was 0.98. The light control model was able to perform dynamic acquisition of the light saturation point and provide a theoretical basis for the efficient and accurate control of the greenhouse light environment.