Closing the loop: AI-driven integration of multi-omics and phenomics for systematic resilient crop engineering

Area of Science:

• Agricultural Science
• Computational Biology
• Genetics

Background:

• Climate change threatens global food security, demanding advanced crop improvement strategies beyond traditional breeding.
• Existing methods often fail to capture complex genotype-environment-management interactions crucial for crop resilience.

Purpose of the Study:

• To review the edge-to-cloud closed-loop framework for AI-driven crop engineering.
• To highlight the transition from predictive models to generative AI for designing stress-resilient crops.

Main Methods:

• Evolution of statistical models to deep representation learning, including transformer architectures and graph neural networks.
• Deployment of multimodal fusion algorithms to analyze complex stress responses.
• Application of generative AI (GAI) and foundation models for designing novel proteins and gene circuits.

Main Results:

• Deep learning models effectively decode complex genotype-environment-management dynamics.
• GAI accelerates the design of de novo stress-resilient traits.
• The framework integrates multi-omics, real-time phenomics, and synthetic biology for holistic crop analysis.

Conclusions:

• Systemic integration of AI transforms crop improvement into a predictive engineering discipline.
• Future directions include pan-genomic foundation models, physics-informed AI, crop digital twins, and autonomous labs.
• This approach is vital for developing resilient cultivars for future climates.