抄録
Wheat is a globally important food crop, and research on its drought and salinity tolerance is crucial for ensuring food security. In recent years, the rapid development of artificial intelligence (AI) technology in agriculture has provided innovative methods and tools to enhance wheat stress resistance. This paper systematically reviews the progress of AI applications in studying wheat drought and salinity tolerance, focusing on key areas such as genomic data analysis, phenotypic data interpretation, and the analysis of gene-environment interactions. The application of AI has enabled researchers to more efficiently identify key genes associated with drought and salinity tolerance, construct high-precision predictive models, and optimize breeding strategies. Moreover, developing intelligent breeding platforms and real-time monitoring systems has further deepened and broadened research on wheat stress resistance. Despite the promising prospects of AI in this field, challenges remain in improving model accuracy, fostering interdisciplinary collaboration, and promoting research outcomes. This paper aims to provide new insights into wheat drought and salinity tolerance research and explore future directions and priorities.
