The field of crop improvement faces critical challenges considering climate change, increasing population, and constrained resources, necessitating significant innovations. Hybrid crops are recognized for their yield potential and resilience to stress, presenting a viable solution for global food security. Nevertheless, fully realizing their capabilities requires a new approach that integrates innovative methods and data-driven breeding techniques. Current research suggests that leveraging advanced phenomics, genomics, genome editing, and artificial intelligence (AI) could optimize hybrid crop development. Despite progress, gaps remain in fully executing these strategies, and a concerted effort is needed to bridge existing divides between technology and biological insights.

This Research Topic aims to underscore the importance of synergy between biological discovery and technological advancements in shaping future breeding strategies. By prioritizing cutting-edge methodologies and cross-disciplinary collaboration, this Research Topic seeks to push the boundaries of crop science, facilitating the cultivation of highly resilient and productive hybrids that align with sustainable agricultural practices. Key objectives include fostering dialogue and research about innovative breeding approaches, hypothesizing outcomes achieved through genetic, phenotypic, and computational advancements, and ultimately ensuring global food and nutritional security.

To gather further insight into creating sustainable hybrid crops, we welcome articles addressing, but not limited to, the following themes:

• Advances in phenomics platforms for trait dissection under variable environments
• Genetic enhancement for parental line development
• Genomic prediction and selection models tailored for hybrid breeding
• Integration of multi-omics (genomics, transcriptomics, metabolomics) in hybrid crop improvement
• Genome editing technologies for developing mutant male sterile lines, haploid induction, and the use of apomixis for the clonal propagation of elite hybrid lines
• Development, designing, and utilization of pollination control systems (Male sterility, Self-incompatibility, Gynoecy) for efficient and economic hybrid breeding
• AI-driven tools and machine learning applications in hybrid performance prediction and selection
• Novel computational pipelines for managing and interpreting high-dimensional breeding data
• Case studies demonstrating the deployment of integrated approaches in hybrid crops such as maize, rice, wheat, sorghum, horticulture crops, and others.

In addition to original research articles, reviews, perspectives, and other article types accepted in Frontiers in Plant Science are welcome.

Are you interested in contributing a manuscript to this Research Topic?

Advancing Hybrid Crop Efficiency: Genomics, Phenomics, and AI Integration