Abstract: Global climate change and intensive human activities have led to increasing degradation of arable land due to ionic stress, such as salinization and heavy metal contamination, posing a significant threat to food security. The rapid development of artificial intelligence (AI) technology offers a new approach for germplasm innovation for stress adaptation. This review systematically summarizes key advances and applications of AI in germplasm innovation for ionic stress tolerance. In terms of phenomics, multi-dimensional phenotyping systems integrate technologies including RGB imaging, spectral sensing, fluorescence imaging, thermal infrared imaging, and XRF. These are coupled with machine learning, deep learning, and multi-source data fusion to enable early, accurate, and non-invasive identification of ionic stress responses. In smart breeding, machine learning accelerates gene discovery, while pangenomics elucidates genetic diversity. AI-driven prediction of multi-gene regulatory networks and genomic selection significantly enhance breeding efficiency. However, challenges such as insufficient data standardization and sharing, limited prediction accuracy of genotype-environment interaction (G×E) models, and high technology promotion costs still constrain large-scale application. Future efforts focused on data integration, algorithm optimization, and technology popularization are expected to allow AI to play a greater role in the remediation of degraded farmland and in achieving sustainable agricultural development.

Key words: artificial intelligence technology,  ionic stress,  adaptive breeding,  high-throughput plant phenotyping technology,  machine learning