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.