抄録
With the deep integration of big data and artificial intelligence (AI) technologies in agricultural biology, molecular regulation research on crop starch synthesis has entered a new era of data-driven intelligence. This paper provides a systematic review of recent advances in big data- and AI-assisted gene mining for potato starch synthesis, focusing on the integration strategies of multi-omics data—including genomics, transcriptomics, and metabolomics—and their roles in identifying key functional genes. By introducing machine learning and deep learning models, it enables the efficient screening of starch synthesis-related genes and regulatory networks from large-scale omics datasets, offering new technological pathways to elucidate the genetic basis of starch biosynthesis in potatoes. Furthermore, the study explores data-driven molecular design and precision breeding models, highlighting the potential of AI algorithms in optimizing starch content and quality. Based on representative case studies, it summarizes major achievements and existing technical challenges in the field, while outlining future directions in algorithm optimization, data standardization, and cross-scale breeding system construction. Overall, this review aims to provide a systematic overview and methodological framework for potato starch synthesis research empowered by big data and AI, serving as a scientific reference and technical foundation for subsequent functional gene discovery and precision breeding practices.
