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

作者：Yue Sun, Qiang Ju, Yiyang Li, Linyi Li, Yuhang Wang, Juan Yang, Tingting Qian · 发表于：Computers and Electronics in Agriculture · 年份：2025 · DOI：10.1016/j.compag.2025.111025 · 被引用次数：4 · 研究领域：Greenhouse Technology and Climate Control、Irrigation Practices and Water Management、Leaf Properties and Growth Measurement

• 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 c...