Generative AI for Drought-Resistant Crop Design

Abstract

Food security risks are posed by climate change-induced droughts, and thus, new methods of crop enhancement are required. This paper outlines a generative AI model to be used in creating drought-tolerant crop varieties through predicting the best genetic compositions and phenotypes. Our system will incorporate diffusion models with evolutionary algorithms that produce new designs of virtual plants, which are trained on multi-omics data (genomics, transcriptomics, phenomics) of 15 drought-resistant species. The framework attains 92% accuracy in the evaluation of the plant water-use efficiency (validated with Arabidopsis thaliana mutants) and can put forward candidate gene-editing targets with 35% increased survival rates in artificial drought experiments in comparison with conventional breeding. The main innovations consist of: (1) a federated learning-based 3D simulator of plants growing that models trait performance at each stage of development, (2) a federated learning architecture that allows consortium members to work cooperatively and keep their data secure, and (3) recommended CRISPR guide RNAs that are incorporated into the generative results. The experimental data on constrained conditions show that the wheat variants developed by AI achieve a yield stability of 85% at a water cut of 40 percent, compared to commercial drought-tolerant strains by 22 percent. The study creates a game-changing diagram in hastened climate-adaptive crop development, more closely connecting computational biology to precise farming.