Abstract
In recent years, significant progress has been made in big data analysis and intelligent breeding technology for crop molecular breeding, greatly enhancing agricultural productivity. This article systematically reviews the current research status, application examples, technical bottlenecks, future directions in crop molecular breeding, big data analysis, and intelligent breeding technology. First, it introduces the definition, historical evolution, and current applications of crop molecular breeding in modern agriculture. Next, it elaborates on applying big data technology in crop breeding, including methods for the integrated analysis of genomic, phenotypic, and environmental data. Subsequently, it focuses on intelligent breeding technologies, particularly the application of machine learning, artificial intelligence, and gene editing in precision breeding, and discusses the key role of high-throughput phenotyping. The article further proposes a comprehensive breeding strategy that integrates big data and intelligent technologies, leveraging multi-source data integration and analysis to promote breeding precision and showcases successful case studies of practical applications. Finally, it discusses the major challenges currently faced in the field and explores future research directions and innovation potential. This article aims to provide a reference for crop breeding research, supporting the further development of related technologies.
