Data-driven localization of the TOMGRO model: Cultivar-specific parameter optimization for Shanghai greenhouse tomato production

• Adapted reduced TOMGRO model for four local tomato cultivars in Shanghai greenhouses. • Sobol’s sensitivity analysis and Bayesian optimization were applied for model calibration. • Achieved model accuracy of R 2 > 0.94 for growth and yield predictions. • 3D digitizing method and Deep-Learning segmentation algorithm were used in data acquisition. Crop models are an integral component in greenhouse control systems, enabling the simulation of plant responses to environmental conditions and facilitating optimal operational decisions for high productivity with low energy use. However, existing crop models often lack transferability beyond their original development conditions. Additionally, cultivar-specific parameterization remains challenging, as some parameters can be empirically determined while others require complex calibration. This study adapted the reduced TOMGRO model to simulate growth and yield for four local tomato cultivars under Shanghai greenhouse conditions. Through Sobol’s global sensitivity analysis and Bayesian optimization, four highly influential parameters were identified and optimized, including growth efficiency (E), maintenance respiration coefficient (r m ), extinction light coefficient (K), and leaf quantum efficiency (Q e ). This combined approach provides an effective framework for model calibration, with the calibrated model achieving an average R 2 > 0.94 for node number, plant dry weight, fruit dry weight, and leaf area index predictions in all cultivars. Model validation using 2023–2024 greenhouse data confirmed model effectiveness for the target variables (average R 2 > 0.92 for cultivar QX and > 0.88 for LZ), whereas the model showed limitations in simulating mature fruit growth. This calibrated model offers reliable predictions of key growth variables, informing both plant breeding and greenhouse management.