摘要Accurate predictions and representations of plant growth patterns in simulated and controlled environments are important for addressing various challenges in plant phenomics research.This review explores various works on state-of-the-art predictive pattern recognition techniques,focusing on the spatiotemporal modeling of plant traits and the integration of dynamic environmental interactions.We provide a comprehensive examination of deterministic,probabilistic,and generative modeling approaches,emphasizing their applications in high-throughput phenotyping and simulation-based plant growth forecasting.Key topics include regressions and neural network-based representation models for the task of forecasting,limitations of existing experiment-based deterministic approaches,and the need for dynamic frameworks that incorporate uncertainty and evolving environmental feedback.This review surveys advances in 2D and 3D structured data representations through functional-structural plant models and conditional generative models.We offer a perspective on opportunities for future works,emphasizing the integration of domain-specific knowledge to data-driven methods,improvements to available datasets,and the implementation of these techniques toward real-world applications.