抄録
Potatoes are a globally significant staple crop, with starch as its primary economic product, widely utilized across the food, pharmaceutical, and industrial sectors. Enhancing both the yield and quality of potato starch is a critical goal in modern agriculture. This paper provides a comprehensive analysis of the interactions between environmental factors—such as soil characteristics, temperature, and humidity—and key genes involved in starch biosynthesis, including GBSS, SBE, and AGPase. Particular attention is given to how environmental conditions modulate gene expression and enzyme activity through complex gene regulatory networks, thereby influencing starch synthesis and structural optimization. The paper also explores the integration of big data analytics and artificial intelligence, especially machine learning and deep learning techniques, in decoding environment-gene interactions. These technologies facilitate large-scale data analysis, the optimization of cultivation conditions, and the implementation of precision agriculture strategies. Case studies demonstrate that precision management based on environment–gene interaction analysis can substantially improve potato starch productivity and quality. This paper ultimately aims to establish a theoretical and technical foundation for intelligent, data-driven agricultural practices, offering actionable insights for optimizing potato cultivation systems.
