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Enhancing Greenhouse Efficiency: Integrating IoT and Reinforcement Learning for Optimized Climate Control.

Manuel Platero-Horcajadas1, Sofia Pardo-Pina2, José-María Cámara-Zapata2

  • 1Informática Industrial y Redes de Computadores (I2RC), University of Alicante, 03690 Alicante, Spain.

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Summary

This study integrates Internet of Things (IoT) and reinforcement learning (RL) for automated greenhouses. This combination optimizes crop management, reduces labor costs, and saves energy compared to traditional methods.

Keywords:
IoTgreenhouse energy managementreinforcement learningsmart agriculture

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Area of Science:

  • Agricultural Technology
  • Artificial Intelligence
  • Automation Systems

Background:

  • Automated greenhouses require skilled technicians for optimal environmental control.
  • Current systems rely heavily on human expertise for installation, maintenance, and fine-tuning.
  • Challenges exist in efficiently managing complex environmental parameters for diverse crops.

Purpose of the Study:

  • To integrate Internet of Things (IoT) data acquisition with reinforcement learning (RL) for automated greenhouse optimization.
  • To develop and validate a model for efficient and adaptive greenhouse management.
  • To reduce human intervention and labor costs in agricultural enterprises.

Main Methods:

  • Implementation of IoT protocols for real-time data acquisition in an industrial greenhouse.
  • Application of reinforcement learning (RL) methodologies for optimizing environmental control strategies.
  • Testing the integrated system on industrial hemp cultivation under the guidance of an agronomic technician.

Main Results:

  • The integrated IoT and RL model effectively managed and optimized greenhouse operations.
  • The system demonstrated adaptability to different crop types and specific agronomic strategies.
  • RL-based control maintained selected temperatures and achieved energy savings over classical methods.
  • Reduced need for constant human intervention, enhancing operational efficiency and scalability.

Conclusions:

  • The integration of IoT and RL technologies presents an effective solution for automated greenhouse management.
  • This approach enhances efficiency, reduces operational costs, and increases scalability in agriculture.
  • The model's ability to adapt and optimize environmental conditions validates its practical application in commercial farming.