The field of plant systems and synthetic biology is being revolutionized by transformative advances in computational biology, machine learning, and multi-scale modeling, leading to an unprecedented capacity to predict plant growth and development. The proliferation of high-throughput phenotyping, multi-omics technologies, and expansive environmental datasets allows researchers to transcend traditional descriptive analyses and pursue dynamic, systems-level insights. Recent studies have demonstrated the power of integrating genomic, physiological, and ecological data within sophisticated computational frameworks, resulting in significant breakthroughs such as improved yield prediction and enhanced stress resilience. However, the landscape remains challenged by persistent gaps in the seamless integration of heterogeneous datasets, robustness across biological variability, and the translation of complex models into tools that are mechanistically rigorous and actionable for real-world application.

This Research Topic aims to accelerate progress in predictive plant science by highlighting transformative computational frameworks that can forecast plant growth, development, and responses to diverse environments. The primary objective is to gather interdisciplinary work that pioneers the use of artificial intelligence, digital twins, hybrid simulations, and integrative modeling platforms. By centering on innovative frameworks that enable explicit links between genotype and phenotype and deliver interpretable, robust predictions, this Research Topic aspires to empower actionable strategies in plant breeding, crop management, and sustainable agriculture. Key questions include: How can empirical biological streams be efficiently incorporated into predictive simulations? Which algorithms best capture the complexity of plant systems across biological scales? And to what degree can these models inform critical decisions in plant science and agriculture?

The scope of this Research Topic is delimited to computational and modeling approaches that demonstrate predictive or prescriptive value for managing and understanding plant development and adaptation. While contributions at the intersection of experimental, theoretical, and applied research are welcome, emphasis is placed on models and tools that foster dynamic forecasting, meaningful biological interpretation, and direct real-world relevance. To further advance insight at the nexus of modeling and plant biology, we welcome manuscripts on, but not limited to, the following themes:

o Artificial intelligence and machine learning approaches for predicting plant traits
o Multi-scale and hybrid models integrating genomic, physiological, and environmental data
o Digital twins and integrative simulation platforms for plant systems
o Computational tools for decision support in crop management and breeding
o Predictive analytics for growth regulation, stress adaptation, and yield optimization
o Methods bridging empirical research with simulation and mechanistic modeling

We invite original research articles, reviews, methods papers, perspectives, and data reports relevant to these themes.

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Transformative Computational Approaches for Predicting Plant Growth and Development