Precision Plant Breeding: Integrating Genome Editing with Artificial Intelligence- A Review
B. L. Santhosh *
Raitha Samparka Kendra, Singatagere, Office of Assistant Director of Agriculture Kadur, Department of Agriculture, Karnataka, India.
Nakka Amulya
Department of Genetics and Plant Breeding, Agriculture University, Jodhpur, India.
Madhuri Singh
Department of Genetics and Plant Breeding, College of Horticulture and Research Station, Ramanujgang (C.G.), India.
C. Varshitha
Department of Genetics and Plant Breeding, University of Agricultural Sciences, Bangalore, India.
Biju Sidharthan
Department of Plant Breeding and Genetics, College of Agriculture, Vellanikkara, Thrissur 680656, Kerala Agricultural University, India.
Vijay Kumar
Department of Genetics and Plant Breeding, School of Agricultural Sciences, IIMT University, Meerut, Uttar Pradesh, India.
Prabha Banjarey
Department of Genetics and Plant Breeding, BTC CARS Bilaspur, IGKV Raipur (C.G.), India.
Udit Prakash
Department of Genetics and Plant Breeding, Agriculture University, Jodhpur, India.
*Author to whom correspondence should be addressed.
Abstract
Precision plant breeding seeks to link biological target discovery with the development and evaluation of useful alleles in crop improvement. Genome-editing platforms, including CRISPR–Cas nucleases, base editors, prime editors, multiplex editing and cis-regulatory editing, enable targeted genomic alterations; however, their successful use depends on appropriate target choice, editing performance and phenotypic validation. Artificial intelligence (AI) offers complementary approaches for integrating pangenomic, multi-omics, phenotypic and environmental data to prioritise genes and regulatory sequences, predict guide-RNA activity and off-target effects, and support selection across genetic backgrounds and environments. This review examines the potential of combining these methods within a design–build–test–learn framework that connects edit design with molecular verification, high-throughput phenotyping, genomic prediction and conventional breeding operations. Examples discussed include susceptibility-gene and promoter editing for disease resistance, regulatory interventions associated with stress responses, nutritional modification and the emerging application of AI-designed nucleases in rice. Although these advances demonstrate technical feasibility, widespread implementation remains constrained by plant-specific data scarcity, differences among genotypes and environments, transformation and regeneration bottlenecks, complex trait architecture, unintended editing outcomes and regulatory diversity. The review emphasises prospective model validation, causal interpretation, pangenome-aware design, rigorous molecular characterisation and replicated field evaluation. It concludes that AI-guided genome editing can support crop improvement when used alongside quantitative genetics, breeder-defined objectives and responsible governance, rather than as a substitute for empirical breeding evidence.
Keywords: Artificial intelligence, base editing, CRISPR–Cas, crop improvement, genomic selection, high-throughput phenotyping, machine learning, pangenomes, precision plant breeding, prime editing