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实现智能农业的高效物联网通信:一个深度学习框架

Ghada Alturif1, Wafaa Saleh2, Alaa A El-Bary3,4,5

  • 1Department of Social Work, College of Humanities and Social Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

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概括

这项研究优化了物联网 (IoT) 通信,以实现智能农业. 它提高了关键作物健康因素的数据准确性和传输效率,最大限度地提高了能源效率和数据吞吐量.

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科学领域:

  • 农业技术 农业技术
  • 计算机科学 计算机科学

背景情况:

  • 现代农业越来越依赖物联网 (IoT) 设备进行实时农场管理.
  • 智能农业利用相互连接的传感器,提供有关作物生长至关重要的环境因素的细粒度数据.

研究的目的:

  • 调查和优化农业物联网网络的通信策略.
  • 为了提高关键农业参数的数据传输效率和准确性.

主要方法:

  • 提出了一个整合拉格朗奇优化和深度卷积神经网络 (DCNN) 的模型.
  • 专注于优化物联网传感器 (测量温度,湿度,土壤水分) 和中央系统之间的通信距离.
  • 旨在最大限度地提高能源效率和数据吞吐量.

主要成果:

  • 综合方法提高了农业物联网通信网络的性能.
  • 展示了一种提高数据传输效率和准确性的方法.
  • 在数据速率和能源效率之间取得了平衡.

结论:

  • 优化物联网通信对于弹性智能农业生态系统至关重要.
  • 拟议的战略支持动态环境调整,以获得最佳的作物健康和产量.
  • 数学优化和深度学习集成提升了农业物联网能力.