以知识为导向的适应性时空图对比学习框架:基于电子医疗记录的区域作物疾病预测
Chang Xu1, Yiding Zhang2, Lei Zhao3
1College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China.
概括
通过使用新的KAST-Graph框架,提高了准确的区域作物疾病预测. 这种智能农业方法分析植物电子医疗记录 (PEMR) 以获得更好的早期预警和预防策略.
科学领域:
- 农业科学 农业科学
- 数据科学数据科学数据科学
- 计算机科学 计算机科学
背景情况:
- 由于复杂的动态和数据限制,作物疾病预测面临着挑战.
- 现有的智能农业方法在实时,准确的区域预测方面扎.
- 植物电子医疗记录 (PEMR) 为疾病分析提供了一个新的大数据源.
研究的目的:
- 为实时区域作物疾病预测开发一个强大的框架.
- 利用工厂电子医疗记录 (PEMR) 进行增强的时空预测.
- 为了解决数据采集,成本和疾病传播复杂性的局限性.
主要方法:
- 提出了一个以知识为导向的适应性时空图对比学习框架 (KAST-Graph).
- 量化了区域疾病发生情况,并将其建模为时空图表预测.
- 集成的适应性,地理信息相邻矩阵和对比的学习增强方案.
主要成果:
- 在最先进的基线上,KAST-Graph表现出了优越的性能.
- 在PEMR的大数据上取得了出色的时空挖掘结果.
- 报告的最佳MAE (5.71),RMSE (9.50) 和MAPE (4.56%) 的得分.
结论:
- KAST-Graph显著提高了区域作物疾病预测能力.
- 该框架增强了对杂和不完整数据的稳定性.
- 这项研究为智能农业的早期预警和预防提供了关键的工具.
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