Abstract: The incorporation of multi-omics strategies, namely genomics, transcriptomics, proteomics, metabolomics, and epigenomics, has been instrumental for promoting crop improvement by providing comprehensive views of the molecular processes driving complex agricultural traits, including enhanced stress tolerance, yield, and nutritional quality. This review presents an overview of the computational methods and tools currently used to analyze and integrate multi-omics data in crops. We then systematically classify them according to integrative strategies (early, intermediate, and late), and analytical methodologies (statistical, machine learning, network-based). Recent advancements in deep learning and explainable AI for predictive trait modeling are highlighted. It also discusses key knowledge gaps, including the under-representation of minor and climate-resilient crops, as well as challenges posed by data heterogeneity, scalability, and field-level validation. Through a newly proposed classification and evaluation framework, the aim of this review is to provide guidelines for researchers to choose computational pipelines and pave the way for future research on data-driven crop improvement and sustainable agriculture.
Kannan et al. (2025) studied this question.