随着全球气候变化与人类活动加剧, 盐碱化与重金属污染等离子逆境导致的耕地退化日益严重, 威胁粮食安全。人工智能技术的快速发展为离子逆境适生种质创新提供了新范式。该文系统综述了人工智能在离子逆境适生种质创新中的主要进展与应用。在表型组学方面, 多维度表型组采集系统整合红绿蓝成像(RGB)、光谱传感、荧光成像、热红外成像与X射线荧光光谱(XRF)等技术, 结合机器学习、深度学习与多源数据融合, 可实现对离子逆境胁迫表型的早期、精准与无损识别。在智慧育种方面, 人工智能应用于基因挖掘、遗传多样性解析、多基因调控网络预测与全基因组选择等方面, 显著提升了育种效率。然而, 当前仍面临数据标准化与共享机制不完善、基因型-环境互作(G×E)模型预测精度有待提升以及技术推广成本高等挑战, 制约了人工智能技术的规模化应用。展望未来, 随着数据整合能力不断增强、算法持续优化以及技术普及程度的不断提高, 人工智能有望在退化耕地治理与农业可持续发展中发挥更大作用。

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 huge threat to world 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 the field of phenomics, multi-dimensional phenotyping systems integrate technologies including red-green-blue imaging, spectral sensing, fluorescence imaging, thermal infrared imaging, and X-ray fluorescence spectrometer. 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 the field of smart breeding, machine learning accelerates gene discovery, along with pangenomics to elucidate genetic diversity. AI-driven prediction of multi-gene regulatory networks and genomic selection have significantly improved 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 are still constraining the large-scale application of the AI-enabled technologies. We expect that future efforts with focus on data integration, algorithm optimization, and technology popularization will allow AI to play a greater role in the remediation of degraded farmland and in achieving sustainable agricultural development.