边缘计算智能灌控制器使用LoRaWAN和LSTM进行预测性控制缺口灌
Carlos Cambra Baseca1, Rogério Dionísio2,3, Fernando Ribeiro2,3
1Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Digitalizacion, Escuela Politecnica Superior, Universidad de Burgos, Av. Cantabria s/n, 09006 Burgos, Spain.
Sensors (Basel, Switzerland)
|November 27, 2025
概括
这项研究介绍了一种支持物联网的智能灌系统,使用边缘计算和机器学习来优化杏仁作物的用水量. 该系统通过预测土壤水分和管理赤字灌,甚至在没有互联网接入的情况下实现可持续农业.
科学领域:
- 农业工程 农业工程
- 环境科学 环境科学
- 计算机科学 计算机科学
背景情况:
- 气候变化正在限制灌用水的可用性,特别是在地中海地区.
- 可持续农业需要高效的水资源管理技术.
- 精准农业为优化农业资源利用提供了解决方案.
研究的目的:
- 为智能灌系统开发一个支持物联网的边缘计算模型.
- 为了提高杏仁种植的精准农业.
- 为了有效地实施受控赤字灌 (CDI) 战略.
主要方法:
- 利用物联网传感器收集气象,土壤湿度和作物数据.
- 开发了一种混合机器学习 (ML) 模型,用于实时预测土壤水分.
- 实施了边缘计算方法,避免对数据处理的云依赖.
主要成果:
- ML模型准确预测土壤水分,用于智能灌.
- 该系统可实现控制缺水灌 (CDI),减少35%的作物蒸发转化 (ETc).
- 在没有云计算的情况下实现了实时作物异常检测.
结论:
- 拟议的物联网支持的边缘计算模型提高了杏仁种植的灌效率和可持续性.
- 这种方法促进了精确的水资源管理,即使在缺乏互联网连接的偏远地区.
- 该研究是将ML算法集成到农业灌策略中的一个基本步骤.
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