Curvature-based approach to determining the optimal temperature regulation ranges for greenhouse peppers across growth stages

In protected agriculture, extreme temperatures can cause irreversible damage to crops, making temperature regulation a critical component of greenhouse environmental management. The dynamic optimization of the temperature ranges serves as the core strategy to enhance production stability. Consequently, identifying the optimal temperature ranges is pivotal for maximizing greenhouse production efficiency. This study proposes a novel method for determining the optimal regulation ranges throughout the multiple growth stages of greenhouse-grown peppers, incorporating curvature theory. A nested experiment was designed to obtain the photosynthetic rate (Pn) of peppers during the multiple growth stages under variable temperature, CO₂ concentration, and photosynthetic photon flux density. A photosynthetic rate prediction model was then constructed using a backpropagation neural network optimized by a genetic algorithm, with an R² of 0.9812 and an MSE of 1.35 μmol/(m²·s). The prediction model was subsequently discretized and applied to calculate the Gaussian response surface of Pn. Finally, the U-chord algorithm and the random restart hill-climbing method were employed to precisely define the boundaries of the temperature regulation ranges. Practice demonstrated that the average dry weight of pepper fruits in the experimental group was 96.83% higher than that of the no-operation regulation group and 243.65% higher than the fixed threshold group. This method not only enhances pepper growth but also exhibits superior regulatory tolerance. Its innovative temperature regulation strategy provides crucial technical support and establishes a reliable decision-making basis for the precise environmental management of greenhouse crops in protected agriculture.