Abstract:
Currently, approximately 4.5 billion people in developing countries consider bread wheat (Triticum aestivum L.) as a staple food
crop, as it is a key source of daily calories. Wheat is, therefore, ranked the second most important grain crop in the developing
world. Climate change associated with severe drought conditions and rising global mean temperatures has resulted in sporadic
soil water shortage causing severe yield loss in wheat. While drought responses in wheat crosscut all omics levels, our understanding of water-deficit response mechanisms, particularly in the context of wheat, remains incomplete. This understanding can
be significantly advanced with the aid of computational intelligence, more often referred to as artificial intelligence (AI) models,
especially those leveraging machine learning and deep learning tools. However, there is an imminent and continuous need for
omics and AI integration. Yet, a foundational step to this integration is the clear contextualization of drought—a task that has
long posed challenges for the scientific community, including plant breeders. Nonetheless, literature indicates significant progress in all omics fields, with large amounts of potentially informative omics data being produced daily. Despite this, it remains
questionable whether the reported big datasets have met food security expectations, as translating omics data into pre-breeding
initiatives remains a challenge, which is likely due to data accessibility or reproducibility issues, as interpreting omics data poses
big challenges to plant breeders. This review, therefore, focuses on these omics perspectives and explores how AI might act as an
interface to make this data more insightful. We examine this in the context of drought stress, with a focus on wheat.
