人工智能驱动离子胁迫耐受性种质创新:进展与挑战OA

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.