Next-Generation Breeding: Integrating Multi-Omics and Artificial Intelligence for Advancing Nutritional and Food Security
Sreeram Harshitha
Department of Genetics and Plant Breeding, School of Agriculture, Lovely Professional University, Phagwara- 144411, Punjab, India.
Rubby Sandhu *
Department of Genetics and Plant Breeding, School of Agriculture, Lovely Professional University, Phagwara- 144411, Punjab, India.
*Author to whom correspondence should be addressed.
Abstract
Global food and nutritional security are increasingly challenged by rapid population growth, climate change, and the prevalence of micronutrient deficiencies, commonly referred to as hidden hunger. Conventional plant breeding approaches, although successful in improving crop productivity, are often limited by low precision, long breeding cycles, and inefficiency in dissecting complex traits such as yield, stress tolerance, and nutritional quality. In recent years, the integration of multi-omics technologies encompassing genomics, transcriptomics, proteomics, metabolomics, phenomics, and epigenomics has revolutionized crop improvement by enabling a comprehensive understanding of biological systems at multiple regulatory levels. These approaches facilitate the identification of key genes, pathways, and molecular interactions underlying agronomically important traits. Simultaneously, advances in artificial intelligence (AI) and machine learning (ML) have provided powerful tools for analyzing large-scale, high-dimensional datasets generated through omics platforms. AI-driven models enhance predictive accuracy, enable genomic selection, and support data-driven decision-making in breeding programs. The integration of multi-omics with AI has significantly improved the efficiency of biofortification strategies aimed at enhancing micronutrients such as iron, zinc, and protein in staple crops, while also contributing to yield improvement and climate resilience. This review synthesizes recent advancements in multi-omics and AI applications in plant breeding, with a particular focus on their role in improving nutritional traits and ensuring sustainable food systems. It also discusses key challenges, including data integration complexities, computational limitations, and phenotyping bottlenecks, along with future prospects for precision breeding. The convergence of these technologies represents a paradigm shift toward predictive, efficient, and sustainable crop improvement strategies.
Keywords: Multi-omics, artificial intelligence, machine learning, genomics, phenomics, biofortification, nutritional security, precision breeding