Advancing Plant Breeding Through Pangenomics and Multi-Omics Integration: Toward AI-Driven Predictive Models for Crop Improvement

The rapid advancement of multi-omics technologies and artificial intelligence (AI) is reshaping the landscape of plant breeding and genomics. By integrating genomic, transcriptomic, proteomic, and metabolomic data, researchers are now able to develop a more comprehensive understanding of plant traits and their complex genetic and environmental interactions. AI-driven predictive models further accelerate these insights, providing powerful tools for genomic selection, trait prediction, and genetic improvement in crop species.

Despite significant progress, challenges remain in fully integrating multi-omics data with AI techniques to create predictive models that are both accurate and applicable to breeding programs. The increasing complexity of genetic data, environmental factors, and phenotypic traits makes the development of effective models a critical task. Moreover, translating these models into practical breeding strategies that improve crop resilience, productivity, and sustainability is essential for addressing global food security challenges.

This Research Topic aims to explore the intersection of multi-omics integration and AI-driven prediction in plant breeding, focusing on how these technologies can enhance our understanding of trait genetics, improve genomic selection, and accelerate genetic gains. We invite contributions that provide insights into how AI and multi-omics can be applied to tackle practical breeding challenges, such as improving disease resistance, drought tolerance, and nutrient use efficiency in crops.

We encourage researchers to submit original research articles, methods, reviews, and perspectives on the following topics:

- Integration of genomic, transcriptomic, proteomic, and metabolomic data for multi-omics approaches in plant breeding

- AI-based predictive models for trait identification and genomic selection in crops

- Advances in genomics-assisted breeding, marker-assisted selection, and genetic gain acceleration

- The role of environmental interactions in predicting plant performance and breeding outcomes

- Case studies of successful AI-driven breeding programs and their practical implications

- Exploring the use of functional genomics and genomic editing technologies in crop improvement

- Addressing biological complexities and improving AI models for better trait prediction in diverse environmental conditions

- Data-driven breeding pipelines for enhancing crop resilience and productivity

- Utilizing AI to predict and analyze genetic-environmental interactions for sustainable breeding strategies

- Development of new phenotyping techniques and AI tools to assess complex traits in plants

The potential of multi-omics and AI in sustainable agriculture, addressing SDGs 2 (Zero Hunger), 12 (Responsible Consumption and Production), and 15 (Life on Land).

This Research Topic seeks to foster collaboration among plant scientists, breeders, and computational biologists to advance the use of multi-omics and AI in breeding programs, ultimately contributing to food security, crop resilience, and sustainable agriculture practices in the face of global challenges.

Authors who are uncertain about the relevance of their work to this topic are encouraged to reach out to the editors for guidance on submission suitability.

Article types and fees

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

• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• ... 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:

• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Review
• Systematic Review
• Technology and Code

Keywords: AI-driven prediction, multi-omics integration, crop resilience, genomic selection, functional genomics

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.