From mechanistic-driven to data-driven: A review of the evolution of crop models

The convergence of climate change,resource scarcity, and rising global food demand necessitates advanced tools for sustainable agricultural intensification.Traditional farming practices, often based on static guidelines,are increasingly inadequate to manage the nonlinear and interactive effects of multiple stressors. Crop models—originally mechanistic, process-based simulators—have evolved into hybrid, data-integrated systems that support precision and intelligent agriculture. This review traces their evolution from early physiological simulations to contemporary paradigms combining mechanistic interpretability with machine learning adaptability,and examines applications in crop growth simulation, management optimization and strategic decision-making. Persistent challenges, including parameter overfitting,computational demands and limited cross-regional transferability, highlight the need for “mechanism-guided, data-enhanced” approaches that anchor interpretability in physiological knowledge while leveraging data-driven flexibility. This synthesis provides both the conceptual and technical foundation for the development of next-generation crop models, offering theoretical support for more precise and adaptive decision-making in smart agriculture.