一种无传感器,基于大数据的方法,用于葡萄园的现象学和气象干旱预测
Ginevra Canavera1, Eugenio Magnanini1, Simone Lanzillotta2
1Dipartimento di Scienze delle Produzioni Vegetali Sostenibili, Via Emilia Parmense 84, 29122, Piacenza, Italy.
Scientific reports
|October 5, 2023
概括
一个新的网络应用程序预测葡萄树的现象学和干旱,这对气候变化适应至关重要. 它使用天气数据和葡萄园特征进行准确的预报,而不需要昂贵的传感器.
科学领域:
- 农业科学 农业科学
- 气候科学 气候科学
- 数据科学数据科学数据科学
背景情况:
- 气候变化正在增加葡萄种植中的现象变化和水资源短缺.
- 准确预测葡萄树现象阶段和干旱对于葡萄园管理至关重要.
研究的目的:
- 开发和测试一个基于网络的应用程序,用于预测葡萄藤的现象阶段 (芽芽,开花,芽) 和气象干旱.
- 提供干旱条件的早期预警,以帮助葡萄园管理.
主要方法:
- 利用了埃米利亚罗马尼亚 (Emilia Romagna) 25个葡萄园 (2021-2022) 的两年数据集.
- 采用二进制事件分类和算法选择用于现象学预测.
- 使用蒸发透气和树冠透气模型计算季节性葡萄园水平衡.
- 通过非线性回归模型估计每日树冠用水量.
- 应用了XGBoost模型用于预测和错误评估 (RMSE).
主要成果:
- 该XGBoost模型实现了RMSEs的5.6,2.3和8.3天,分别为芽,开花,和Veraison预测.
- 干旱警告 (轻度/重度) 在与现场数据相比,显示14个案例研究中的9个是正确的.
- 该工具使用基本的天气和葡萄园数据准确地预测了现象学和干旱警告.
结论:
- 开发的网络应用程序提供可靠的现象预报和气象干旱预警.
- 该工具可以适应不同的葡萄园位置,不需要专门的传感器.
- 这项技术支持葡萄种植的气候变化适应战略,通过使主动管理成为可能.
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