In recent years, China’s agricultural development has gradually shifted from digital agriculture to smart agriculture. At the same time, with the participation of AIGC, the decision-making system of smart agriculture is also facing numerous data challenges. In this study, we employed a comprehensive quality improvement approach to ad-dress these challenges. The methodology involves three phases: (1) Detection and removal of data noise through advanced cleaning techniques and preprocessing methods; (2) Unified data standards and formats to ensure seamless integration across di-verse data sources; and (3) Strengthening agricultural infrastructure to prevent data islands and promote equitable data distribution. Our analysis reveals that data noise significantly impacts precision agriculture, leading to biased decisions and resource wastage. Data fog, resulting from heterogeneous data sources and weak inter-source correlations, complicates decision-making processes. Additionally, data islands hinder data sharing and integration, exacerbated by uneven data development across regions. Systematic implementation of standardized quality control protocols is essential for enhancing smart agricultural systems and ensuring sustainable development. This study offers a novel perspective on enhancing data quality in AIGC-driven smart agriculture by integrating the Juran quality improvement model.
Ren et al. (2025) studied this question.