Edge-Computing Smart Irrigation Controller Using LoRaWAN and LSTM for Predictive Controlled Deficit Irrigation

  • 0Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Digitalizacion, Escuela Politecnica Superior, Universidad de Burgos, Av. Cantabria s/n, 09006 Burgos, Spain.

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Summary

This summary is machine-generated.

This study introduces an IoT-enabled smart irrigation system using edge computing and machine learning to optimize water use for almond crops. The system enables sustainable agriculture by predicting soil moisture and managing deficit irrigation, even without internet access.

Area Of Science

  • Agricultural Engineering
  • Environmental Science
  • Computer Science

Background

  • Climate change is limiting irrigation water availability, especially in Mediterranean regions.
  • Sustainable agriculture requires efficient water management techniques.
  • Precision agriculture offers solutions for optimizing resource use in farming.

Purpose Of The Study

  • To develop an IoT-enabled edge computing model for smart irrigation systems.
  • To enhance precision agriculture for almond cultivation.
  • To implement Controlled Deficit Irrigation (CDI) strategies efficiently.

Main Methods

  • Utilized IoT sensors to gather meteorological, soil humidity, and crop data.
  • Developed a hybrid machine learning (ML) model for real-time soil moisture prediction.
  • Implemented an edge computing approach, avoiding cloud dependency for data processing.

Main Results

  • The ML model accurately predicts soil moisture for smart irrigation.
  • The system enables the application of Controlled Deficit Irrigation (CDI) with 35% reduction in crop evapotranspiration (ETc).
  • Real-time crop anomaly detection was achieved without cloud computing.

Conclusions

  • The proposed IoT-enabled edge computing model enhances irrigation efficiency and sustainability in almond farming.
  • This methodology facilitates precise water management, even in remote areas lacking internet connectivity.
  • The study is a foundational step towards integrating ML algorithms into agricultural irrigation strategies.

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