Advances in Multi-Omics and AI-Driven Breeding for Climate-Resilient Crop Plants

Recent technological advances have transformed plant breeding into a data-intensive science. High-throughput sequencing, large-scale phenotyping platforms, and multi-omics profiling now generate vast amounts of biological data across different crops and environments. At the same time, artificial intelligence and machine learning have emerged as powerful tools for integrating complex datasets, uncovering hidden patterns, and enhancing prediction accuracy for complex traits.

Despite progress, the integration of multi-omics data into breeding pipelines remains uneven, and many studies remain not linked to practical crop improvements. There is a growing demand for comprehensive, systems-level methods that connect molecular insights, field phenotyping, and breeding decisions. This Research Topic provides a dedicated platform to unify these advancements and promote translational research across various crop species.

Crop plants are increasingly subjected to complex biotic and abiotic stresses caused by climate change, emerging pathogens, and intensified agricultural practices. These factors substantially reduce crop productivity, stability, and global food security. Although progress in genomics and breeding has enhanced our understanding of plant stress responses, creating durable, stress-resistant cultivars remains challenging. This difficulty primarily arises because stress tolerance is polygenic, involves significant genotype-by-environment interactions, and traditional phenotyping and selection techniques have their limitations.

This Research Topic examines recent advances in multi-omics integration and AI-driven breeding approaches to accelerate the development of crop stress resistance. By integrating genomics, transcriptomics, metabolomics, phenomics, and sophisticated computational methods, it provides innovative frameworks for accurate trait identification, predictive breeding, and data-informed decision-making to improve crops under diverse stress conditions.

This Research Topic welcomes original research, reviews, methods papers, and data-driven case studies that encourage the application of multi-omics and AI-based techniques to develop stress-resistant crops. Contributions may address, but are not limited to, the following themes:

• Integration of genomics, transcriptomics, metabolomics, proteomics, and phenomics.

• AI and machine learning applications for trait discovery, genomic prediction, and selection.

• Breeding for abiotic stress tolerance (heat, drought, salinity, nutrient stress)

• Breeding for resistance to biotic stresses (pathogens, pests, and complex disease systems)

• High-throughput and field-based phenotyping methods

• Functional genomics, gene editing, and validation of stress-related traits

• Translational breeding pipelines and multi-environment studies across crop species.

Submissions that cover various crop plants like cereals, legumes, vegetables, and specialty crops are highly encouraged.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Review
• Systematic Review
• Technology and Code

Keywords: Genomic prediction, Multi-omics integration, Artificial intelligence, Machine learning, Climate-resilient crops, Stress tolerance, Genomic selection, High-throughput phenotyping, Crop improvement

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.