提高早期预测西班牙橄园的作物产量,使用卫星图像和机器学习
M Isabel Ramos1, Juan J Cubillas2, Ruth M Córdoba3
1Department of Cartographic, Geodesic and Photogrammetry Engineering, University of Jaén, Jaén, Spain.
PloS one
|January 15, 2025
概括
这项研究使用机器学习和卫星数据预测季节早期的橄作物和油产. 这种早期预测有助于农场管理,并确保橄业的长期可行性.
科学领域:
- 农业科学 农业科学
- 数据科学数据科学数据科学
- 地理空间分析的研究.
背景情况:
- 准确的作物产量预测对于农场经济稳定至关重要.
- 现有的方法预测产量在赛季末,在做出关键决策后.
- 橄园的早期产量预测是有效管理的必要条件.
研究的目的:
- 开发一种用于早期预测橄作物和橄油产量的模型.
- 为西班牙Jaen省在收获前几个月提供可操作的数据.
- 支持橄油行业所有利益相关者的决策.
主要方法:
- 利用机器学习算法和预测变量分析.
- 来自公共网络服务和空间数据基础设施的综合时间数据.
- 使用谷歌地球引擎处理卫星图像进行地理空间分析.
主要成果:
- 实现了对橄作物和橄油产量的早期预测.
- 预测准确度优于26%的平均绝对误差.
- 预测是在收获活动开始前八个月进行的.
结论:
- 预测作物产量数据的早期可用性非常重要.
- 预测模型证明了多个规模的适用性.
- 该模型是农民,技术人员,研究人员和政府机构的宝贵工具.
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