植物学报2026，Vol.61Issue(4)：588-597,10.DOI:10.11983/CBB25177
人工智能驱动离子胁迫耐受性种质创新:进展与挑战
AI-driven Innovation of Ion Stress-tolerant Germplasm:Progress and Challenges
摘要
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 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 in-cluding red-green-blue imaging,spectral sensing,fluorescence imaging,thermal infrared imaging,and X-ray fluores-cence 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.关键词
人工智能技术/离子逆境/适应性育种/高通量植物表型技术/机器学习Key words
artificial intelligence technology/ionic stress/adaptive breeding/high-throughput plant phenotyping technology/machine learning引用本文复制引用
闫慧莉,何振艳,秦雅婷,周运转,张怀,李斌峰,公保南加,张生燕,许文秀,宋晓彦..人工智能驱动离子胁迫耐受性种质创新:进展与挑战[J].植物学报,2026,61(4):588-597,10.基金项目
青海省重点研发与转化计划(No.2025-NK-126)、国家重点研发计划(No.2023YFD1200700,No.2023YFF1001304)、科技创新2030-重大项目(No.2023ZD04072)和国家自然科学基金(No.U23A20183) （No.2025-NK-126）
