Integrating Multi-omics and Artificial Intelligence for Next-generation Crop Improvement: Advances, Constraints and Future Directions

Abstract

Crop improvement increasingly depends on extracting useful breeding signals from data that span DNA sequence variation, gene regulation, molecular phenotypes, high-throughput field measurements and environmental exposure. Multi-omics can connect genotype to phenotype through intermediate biological layers, while artificial intelligence (AI) and machine-learning methods can model nonlinear, high-dimensional relationships that are difficult to represent with conventional approaches. Yet greater data volume and model complexity do not automatically translate into greater genetic gain. This critical narrative review evaluates how genomics, pangenomics, transcriptomics, epigenomics, proteomics, metabolomics, phenomics and environmental covariates are being integrated with statistical learning, machine learning and deep learning for crop improvement. Literature was selected from accessible scholarly databases and indexes through 13 June 2026, with emphasis on peer-reviewed studies that permit evaluation of predictive value, biological interpretation and breeding relevance. The evidence is strongest where additional modalities capture non-redundant information that is biologically proximal to the target trait or environment, as demonstrated in hybrid prediction, stress adaptation, grain-quality analysis and environment-aware genomic prediction. Conversely, classical genomic best linear unbiased prediction and related models remain competitive in many settings, particularly when sample size is modest, relationships among individuals dominate prediction, or nonlinear signal is weak. Reported AI advantages are sensitive to validation design, relatedness between training and test sets, tissue and developmental stage, environmental transfer, missing modalities and hyperparameter tuning. Pangenomes, single-cell regulatory maps and interpretable multimodal models broaden the biological search space, but evidence for routine breeding utility remains less mature than their mechanistic promise. The most defensible path forward is therefore not unrestricted model escalation, but decision-focused integration: biologically informed feature representation, prospective multi-environment validation, explicit uncertainty, robust missing-data handling and functional validation of discovered mechanisms. Multi-omics and AI are most likely to accelerate crop improvement when evaluated against breeding decisions and realised genetic gain rather than prediction accuracy alone.