A cooperative regulation method for greenhouse soil moisture and light using Gaussian curvature and machine learning algorithms

Abstract

Soil moisture (SM) exerts a significant impact on crop growth, interacting with environmental factors such as temperature, photosynthetic photon flux density (PPFD), and CO2, ultimately affecting crop photosynthesis (Pn). This study employs a nested experimental design to investigate the photosynthetic activity of cucumber seedlings under diverse environmental conditions and establishes a support vector regression (SVR) model for Pn prediction. The SVR model takes temperature, PPFD, SM, and CO2 concentration as inputs and demonstrates a high level of accuracy (The model’s coefficient of determination = 0.9830, root mean square error = 0.9138). Subsequently, through a generalized additive model, the study unveils the significant impact of interactions between SM and PPFD on Pn. Accordingly, this research constructs a Pn response surface based on these two factors and identifies the maximum point of Gaussian curvature on this surface. Polynomial regression is applied to these points, yielding a comprehensive regulation strategy for SM and PPFD. In comparison to traditional methods based on maximizing Pn, this innovative approach reduces Pn by 12.9 % while significantly conserving light (35.59 %) and water (32.80 %) consumption. Although no significant changes are observed in crop physiological traits (plant height, stem diameter, dry weight, fresh weight), substantial variations are noted in irrigation volume and PPFD consumption. Thus, the regulation strategy proposed in this study embodies efficiency and energy conservation in greenhouse crop cultivation.