In response to the challenges facing 21st century agriculture, advanced science and technology approaches need to be developed and adopted to provide improved avenues for growth and food security. Machine learning will be part of this solution. Wheat is a major source of human nutrition. This project aims to develop and test a deep-learning-based AI model for predicting wheat yield component traits from multiple ‘omic data, including genome (Single Nucleotide polymorphism -SNPs) and transcriptome, and identify causal biomolecules leading to high-yield. The resulting model will demonstrate feasibility of applying ML&AI to biological data toward the improvement of crop yield by enabling useful tasks such as predicting varietal productivity in different environments.