AI-Driven Breeding Enhances Stress Tolerance in High-Elevation Extremophytes: A Proof-of-Concept Study with Cross-Component Validation

High-elevation extremophytes exhibit unique survival strategies under harsh climatic conditions, making them attractive targets for sustainable agriculture and climate resilience research. In this study, we present a comprehensive proof-of-concept application that integrates multi-omics data, environmental simulation, and state-of-the-art machine learning techniques to predict and enhance stress tolerance in these resilient species. Our platform combines graph neural networks (GNNs) for modeling gene–environment interactions, digital twin simulations for plant growth prediction, quantum-inspired tensor networks for simulation fidelity, and generative adversarial networks (GANs) for proposing novel gene combinations. Using synthetic data emulating real-world conditions, we demonstrate that our platform can accurately predict plant growth and stress tolerance, with the GNN model achieving a Pearson correlation coefficient of 0.82.

Furthermore, the GAN proposed gene combinations that improved predicted stress tolerance by up to 15%. Implemented as a modular backend and an interactive frontend, the application provides a scalable, data-driven roadmap to revolutionize plant breeding. Our results highlight the potential of AI-driven methodologies to accelerate extremophyte breeding, offering valuable insights into the interplay of genomic and environmental factors under extreme conditions, while emphasizing the need for future validation with real-world data.