AI-enhanced framework for optimizing CRISPR-Cas gene editing in crop biotechnology addressing regulatory challenges and opportunities in global agricultural practices.

Introduction: The integration of CRISPR Cas genome editing with artificial intelligence (AI) offers significant potential for crop biotechnology by supporting more precise and adaptive strategies for trait improvement under complex agricultural and regulatory conditions. However, the global governance of gene edited crops remains highly heterogeneous, creating major challenges for the development of frameworks that can jointly support optimization, uncertainty management, and regulatory alignment. Conventional approaches often lack the ability to account for evolving regulatory requirements and multi source uncertainties in a unified manner. Methods: In this paper, we introduce the Adaptive Regulatory Optimizer (ARO), an AI enhanced framework designed to support CRISPR Cas genome editing in crop biotechnology under biologically, regulatorily, and contextually constrained conditions. The ARO consists of three interconnected modules: the Manifold Constrained Gene Editor, the Agent Driven Regulatory Planner, and the Uncertainty Propagation Filter. Together, these modules embed editing decisions within biologically feasible manifolds, incorporate jurisdiction aware regulatory planning, and model interacting uncertainties associated with gene editing and deployment contexts. The The framework combines constrained optimization refinement, probabilistic uncertainty modeling, and adaptive regulatory planning to provide a structured basis for compliance aware and context sensitive decision support. Results and discussion: Experimental results on the evaluated datasets indicate that the ARO achieves improved performance on the selected metrics relative to the compared methods, while its architecture is explicitly designed to integrate regulatory constraints into the optimization process. These findings suggest that the proposed framework provides a promising foundation for supporting more transparent, adaptive, and analytically grounded decision making in CRISPR Cas applications for crop biotechnology.