一个物联网驱动的预测分析框架,用于在番茄种植中进行动态灌优化
Maung Maung Htwe1,2, Lachezar Filchev1, Ekaterina Batchvarova1
1Climate, Atmosphere and Water Research Institute - Bulgarian Academy of Sciences.
Mathematical biosciences and engineering : MBE
|January 6, 2026
概括
这项研究开发了一种基于物联网的预测模型,用于优化番茄灌,显著减少用水量. 该框架实现了50.84%的节水,增强了智能农业实践.
科学领域:
- 农业工程 农业工程
- 数据科学数据科学数据科学
- 环境科学 环境科学
背景情况:
- 全球粮食安全和水资源短缺是关键的挑战.
- 有效的灌对于可持续农业和作物弹性至关重要.
研究的目的:
- 开发一个物联网 (IoT) 驱动的预测分析框架,用于在番茄种植中进行动态灌优化.
- 准确估计每天的用水需求,尽量减少用水量,同时保持土壤健康.
主要方法:
- 利用来自多传感器物联网部署 (环境和土壤参数) 的综合数据集.
- 应用了严格的数据预处理和功能工程,包括Growing Degree Days (GDD).
- 开发并验证了一个两部分的极端梯度增强 (XGBoost) 回归模型.
主要成果:
- 在XGBoost模型实现了高R2的0.9476预测每日水量.
- 在模拟动态优化中证明了50.84%的潜在节水.
- 在不同的条件下确定了最佳水位,并以高准确度确定了最佳水位.
结论:
- 该框架为灌计划提供了一个复杂的智能层.
- 为有效的精密灌策略提供数据驱动的见解.
- 授权农民减少水浪费,促进可持续的智能农业.
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