在智能农业中AIGC应用的数据质量挑战
Yingxue Ren1,2, Yitong Qu2, Runzeng Gao2
1Business School, Nankai University, Tianjin, China.
Frontiers in artificial intelligence
|October 9, 2025
概括
中国中国中国中国.
科学领域:
- 农业科学 农业科学
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 中国的农业部门正在从数字向智能农业过渡.
- 人工智能生成内容 (AIGC) 集成对智能农业决策系统提出了数据挑战.
研究的目的:
- 为了应对AIGC驱动的智能农业中的数据挑战.
- 为农业数据提出全面的质量改进方法.
- 整合Juran质量改进模型,以提高数据质量.
主要方法:
- 数据噪声检测和删除使用先进的清洁和预处理.
- 统一数据标准和格式,以实现无集成.
- 加强农业基础设施,防止数据岛屿,促进公平分配.
主要成果:
- 数据噪声对精准农业产生重大影响,导致偏见的决策和资源浪费.
- 来自异质来源的数据雾使决策复杂化.
- 数据岛阻碍了数据共享和整合,区域差异加剧了这种情况.
结论:
- 标准化质量控制协议对于增强智能农业系统至关重要.
- 提高数据质量对于AIGC时代的可持续农业发展至关重要.
- 朱兰质量改进模型为数据质量提升提供了一个新的视角.
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