Abstract:

[Objective] In protected grape cultivation, supplementary lighting can improve crop photosynthetic efficiency, regulate plant growth, and improve fruit yield and quality. However, most existing supplementary lighting studies focus on optimizing instantaneous photosynthetic rate or yield-related indicators through fixed or empirical lighting strategies, ignoring the complex trade-off between photosynthetic performance and energy consumption under variable greenhouse conditions. As energy costs rise and sustainable agriculture becomes more important, a dynamic energy-saving supplementary lighting control strategy for greenhouse Shine Muscat grapes based on the optimal photosynthetic efficiency point (OPEP) is proposed in this study, with plant physiological response modeling and multi-objective optimization integrated, to develop an intelligent, energy-saving lighting regulation system that balances crop physiological demands and energy conservation. [Methods] First, a thorough dataset was built by collecting crucial environmental and physiological factors at various stages of the grape veraison phase, such as temperature, photosynthetically active radiation (PAR), and leaf-level photosynthetic rate. The choice of the veraison period is especially crucial, since this stage signifies a key transitional stage in grape growth, when both the demand for carbon assimilation and the sensitivity to the environment are elevated. A modified rectangular hyperbola model was used to describe the nonlinear response relationship between PAR and photosynthetic rate, allowing for precise depiction of light saturation and the law of diminishing returns under high-irradiance conditions. In addition, a D-Sigmoid function was employed to simulate the temperature–photosynthetic rate response, depicting the asymmetric and threshold-dependent physiological characteristics of grape leaves under different thermal conditions. Based on these two sub-models, a light-temperature interaction model was built to reflect the joint effects of environmental factors on photosynthetic performance, enabling more realistic simulation of greenhouse microclimate changes. A multi-objective optimization system was then established with two conflicting aims: (1) maximizing the net photosynthetic rate to ensure adequate carbon assimilation for grapevine growth, and (2) minimizing the energy consumption of supplemental lighting to enhance energy efficiency and cut down operational expenses. To address this issue, the strength pareto evolutionary algorithm 2 (SPEA-2) was employed because of its strong ability to preserve solution variety and approach the Pareto-optimal front in intricate nonlinear search domains. The algorithm repeatedly produced a Pareto frontier that showed the trade-off between photosynthetic performance and energy consumption. Afterward, a sequential search algorithm was utilized to precisely determine the OPEP from the Pareto set. [Results and Discussions] Experiments were conducted under controlled greenhouse conditions to evaluate the efficacy of the proposed approach. The findings showed that, in the grape veraison stage, the suggested dynamic supplementary lighting approach notably enhanced energy utilization effectiveness when compared with traditional fixed-intensity lighting plans. In particular, the consumption of lighting energy was decreased by an average of 34.27%, whereas the decline in photosynthetic rate was restricted to merely 6.60%. This shows that the system effectively prevents excessive compromise of physiological performance when attaining considerable energy savings. A deeper examination of the temporal response curves indicated that the suggested method enabled dynamic adjustments to the lighting intensity based on environmental variations, avoiding needless energy consumption during times of light saturation or when temperature conditions were favorable. This adaptable behavior is especially advantageous in greenhouse settings where microclimatic circumstances can vary rapidly and non-linearly. Moreover, the Pareto frontier produced by SPEA-2 showed a smooth and evenly-spread trade-off surface, validating the robustness of the optimization framework. The recognized OPEP point was found to be situated close to the inflection area of the response curve, where marginal photosynthetic benefits start to decrease significantly in relation to energy input. This shows that the proposed method can approach the physiological efficiency limit of grape leaves when there is combined light and temperature control. [Conclusions] The proposed approach is able to cut down the energy consumption of supplementary lighting while maintaining a relatively high photosynthetic capacity for greenhouse-cultivated Shine Muscat grapes. By combining physiological response modeling with multi-objective evolutionary optimization, this method provides a systematic and interpretable framework for precise lighting control. It efficiently enhances the utilization rate of energy and attains an ideal equilibrium between production advantages and energy usage in protected horticultural systems. Furthermore, this framework may also be applicable to other high-value greenhouse crops and provides a general strategy for smart agricultural energy management under future climate and sustainability limitations. Future research could further integrate other environmental factors, such as humidity and CO₂ concentration, and investigate real-time adaptive control tactics based on reinforcement learning to improve model responsiveness in dynamic greenhouse environments.

Key words: protected grapes, multi-objective optimization, photosynthetic rate, environmental regulation, energy saving, supplemental lighting control