抄録
In the face of increasingly severe global agricultural challenges, enhancing crop yield, disease resistance, and stress tolerance has become a research priority. The rapid advancement of artificial intelligence (AI) technologies offers new opportunities for crop gene research. This paper systematically reviews the latest applications of AI in crop genomics, focusing on its specific use cases in gene data processing, gene-trait association analysis, functional gene prediction, and molecular marker screening. Furthermore, the paper explores the potential of AI in assisting crop breeding, particularly in accelerating the development of high-yield, disease-resistant, and stress-tolerant varieties. The integration of AI technologies with traditional breeding methods and their potential applications are also discussed, along with the technical challenges and data bottlenecks currently faced in research. Finally, the paper envisions the future development of AI in crop gene research and provides several recommendations for future studies. By offering a comprehensive perspective on the role of AI in crop gene research, this review aims to support further advancements in the field.
