抄録
With the continuous advancement of agricultural modernization, the application of big data technologies and gene editing methods in rice breeding and nitrogen fertilizer management has increasingly become a research focus. This study proposes a synergistic optimization strategy that integrates multi-source data analysis, gene editing technologies (such as CRISPR-Cas9), and machine learning and deep learning algorithms to achieve an organic integration of rice variety improvement and nitrogen fertilizer management. Based on whole-genome data of rice, dynamic monitoring information of soil nitrogen, and environmental conditions such as climate and soil properties, a collaborative optimization model was developed to enhance nitrogen uptake efficiency, optimize breeding strategies, and enable precise and intelligent fertilization control. The goal is to maximize nitrogen use efficiency, increase rice yield, and reduce environmental burdens. Empirical analysis and case studies demonstrate that the proposed strategy offers significant advantages in improving crop productivity and promoting sustainable agriculture. The findings provide valuable insights for the future application of smart agricultural technologies.
