Towards smart agriculture: AI-driven prediction of key genes for revolutionizing crop breeding

AI-driven key gene prediction is revolutionizing crop breeding, enhancing precision, efficiency, and sustainability while paving the way for intelligent, data-driven agricultural innovation. The integration of artificial intelligence (AI) into crop breeding is ushering agriculture into a data-driven era of precision practices, fundamentally reshaping the efficiency and accuracy of crop improvement. This review provides an in-depth analysis of recent advances in AI-based key gene prediction within the field of crop breeding. It comprehensively evaluates the application outcomes and potential impacts, encompassing multi-omics data integration, deep learning model construction, key gene prediction, and variety design. Representative models such as SoyDNGP have significantly improved the coefficient of determination (R²) for soybean yield prediction to 0.89-substantially outperforming traditional GBLUP models (R² = 0.72)-through innovative data transformation and analytical strategies, while accurately pinpointing high-yield associated genomic regions such as qYield-08-3. Moreover, AI has successfully identified key genes across various crops, including cotton (fiber development) and maize (nitrogen use efficiency), thereby enabling targeted trait improvement. Nonetheless, future development faces critical challenges, including the standardization of heterogeneous data sources, data security risks, the black-box nature of deep learning models, and limitations associated with small-sample learning. Looking ahead, it is imperative to establish an intelligent breeding loop encompassing AI prediction-gene editing-robotic execution, advance agricultural large language models (Agri-LLMs) for inclusive applications, build sustainable breeding evaluation systems, and empower smallholder farmers through edge computing technologies. Through interdisciplinary collaboration and global data sharing, AI is poised to break through the limitations of traditional breeding and provide essential technological support for global food security and sustainable agricultural development. In essence, this progress follows three core trajectories: (1) a technological paradigm shift from empirical breeding to precision design; (2) multidimensional application value across efficiency, productivity, and sustainability; and (3) the pursuit of an intelligent, green, and inclusive future for agriculture.