Jinhua Cheng , Mengting Luo

Institute of Life Science, Jiyang College of Zhejiang A&F University, Zhuji, 311800, China
Author    Correspondence author
Cotton Genomics and Genetics, 2025, Vol. 16, No. 3   
Received: 28 Apr., 2025    Accepted: 07 Jun., 2025    Published: 29 Jun., 2025

This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Cotton is an important economic crop related to the national economy and people's livelihood, but traditional breeding faces challenges such as long cycle, low efficiency and difficulty in improving yield and quality simultaneously. As a new technology of molecular breeding, genomic selection (GS) improves breeding accuracy and efficiency by utilizing whole genome marker information, and shows great potential in crop breeding. In recent years, the rapid development of artificial intelligence (AI) technology has injected new impetus into agricultural breeding. The application of machine learning and deep learning to crop genome big data analysis is expected to accelerate the breeding process of crops such as cotton. This study reviews the current status and challenges of cotton breeding, the basic principles of genomic prediction breeding, and the application progress of artificial intelligence algorithms in cotton breeding. The research progress of genomic prediction of major cotton traits such as yield, stress resistance and fiber quality is discussed in detail. Typical cases in Australia, the United States and China are cited to analyze the practice of cotton intelligent breeding. The current challenges in data quality and model generalization ability, multi-omics data integration, model interpretability, etc. are analyzed, and the future development direction of the integration of artificial intelligence and genomic prediction is prospected. This study hopes to break through the bottleneck of traditional breeding, improve the efficiency and accuracy of cotton breeding, and cultivate new varieties with high yield, high quality and multi-resistance.

Cotton breeding; Genomic selection; Phenotypic prediction; Deep learning; Intelligent breeding