抄録
The continuous growth in global food demand poses significant challenges for agriculture. Gene editing technology offers new solutions for crop breeding, yet the complexity of genome and multi-omics data interpretation remains a critical bottleneck. In recent years, the remarkable performance of artificial intelligence (AI) in big data processing and analysis has brought new opportunities to gene editing and crop breeding. This paper systematically explores the multi-level applications of AI in agricultural genomics, including data processing and analysis, gene-editing target prediction, crop phenotype prediction, breeding optimization, and automated high-throughput screening. Through case studies, the paper demonstrates how AI enhances the efficiency and accuracy of gene editing, shortens breeding cycles, and examines future development potential and challenges. This work aims to provide reference and inspiration for research and practical applications in related fields.
