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Crop growth models have evolved from initial crop development models to agricultural decision support models, playing an increasingly important role in scientific research, agricultural management, and policy-making. In the paper the development process of crop growth models was firstly reviewed. Based on the main driving factors, the models were categorized into three types: soil factors, photosynthetic factors, and human factors, and comprehensive introductions to each category were provided. Then a comparative analysis of typical models was presented from ten aspects, including model modules, spatiotemporal scales, and range of crop types that can be simulated. Furthermore, the applications of crop growth models in climate change assessment, production management decision support, and resource management optimization were discussed. The challenges faced by these models were also highlighted, such as extreme conditions, complex agricultural landscapes, and model complexity. Based on the comprehensive discussions, two promising directions for the future development of crop growth models were identified: remote sensing data assimilation and twin farming. Remote sensing data assimilation techniques have the potential to significantly enhance the spatial range and accuracy of the simulations, providing more precise information for agriculture. Twin farming, on the other hand, offers virtual replicas of actual farming systems, enabling comprehensive analysis and optimization of crop growth. These research findings provide valuable insights for selecting and improving crop growth models, driving advancements in this field.