一个全面的英国作物产量数据集,包含卫星,天气和土壤类型信息
Evangeline Corcoran1, Daniel P Bebber2, Stelian Curceac3,4
1The Alan Turing Institute, 96 Euston Road, London, NW1 2DB, UK.
Scientific data
|February 20, 2026
概括
农作物产量,气候,土壤和卫星 (CYCleSS) 数据集为机器学习和作物生长模型提供了宝贵的农业数据. 这个匿名数据集集成了产量,天气,土壤和卫星信息,以推进作物产量预测.
科学领域:
- 农业科学 农业科学
- 数据科学数据科学数据科学
- 环境科学 环境科学
背景情况:
- 数据驱动的方法,包括机器学习,对于农业研究中的作物产量预测越来越重要.
- 这些方法需要大量的训练数据集,这对开发准确的预测模型构成了挑战.
- 现有的作物生长模型从补充数据驱动的洞察力中受益.
研究的目的:
- 引入作物产量,气候,土壤和卫星 (CYCleSS) 数据集,这是一个全面的农业研究资源.
- 提供一个大规模的匿名数据集,以促进机器学习模型的训练和作物生长模型的参数化.
- 通过提出一种新的匿名化程序,应对农业研究中的数据隐私挑战.
主要方法:
- 从英格兰934个田间的精确产量数据编制了一个大规模的数据集,涵盖了各种作物.
- 综合卫星衍生的遥感数据,每日天气数据和土壤类型信息.
- 将所有数据调整为10公里网格分辨率,确保空间和时间的一致性.
主要成果:
- CYCleSS数据集包含匿名的作物产量数据,以及对齐的遥感,天气和土壤数据.
- 数据以各种分辨率可获得:天气每天,卫星每5天,作物产量每年.
- 匿名化技术保留了数据的实用性,同时保护了敏感的收益信息.
结论:
- CYCleSS数据集是训练机器学习模型用于作物产量预测的宝贵资源.
- 它还可以用于参数化机械作物生长模型,增强农业研究.
- 匿名化方法为农业研究和实践中的数据共享挑战提供了实际解决方案.
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