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DigiHortiRobot: An AI-Driven Digital Twin Architecture for Hydroponic Greenhouse Horticulture with Dual-Arm Robotic Automation

Author

Listed:
  • Roemi Fernández

    (Centre for Automation and Robotics, UPM-CSIC, Carretera Campo Real Km 0.2, Arganda del Rey, 28500 Madrid, Spain)

  • Eduardo Navas

    (Centre for Automation and Robotics, UPM-CSIC, Carretera Campo Real Km 0.2, Arganda del Rey, 28500 Madrid, Spain)

  • Daniel Rodríguez-Nieto

    (Centre for Automation and Robotics, UPM-CSIC, Carretera Campo Real Km 0.2, Arganda del Rey, 28500 Madrid, Spain
    PhD Program in Automation and Robotics, Universidad Politécnica de Madrid, Calle de José Gutiérrez Abascal 2, 28006 Madrid, Spain)

  • Alain Antonio Rodríguez-González

    (Centre for Automation and Robotics, UPM-CSIC, Carretera Campo Real Km 0.2, Arganda del Rey, 28500 Madrid, Spain)

  • Luis Emmi

    (Centre for Automation and Robotics, UPM-CSIC, Carretera Campo Real Km 0.2, Arganda del Rey, 28500 Madrid, Spain)

Abstract

The integration of digital twin technology with robotic automation holds significant promise for advancing sustainable horticulture in controlled environment agriculture. This article presents DigiHortiRobot, a novel AI-driven digital twin architecture tailored for hydroponic greenhouse systems. The proposed framework integrates real-time sensing, predictive modeling, task planning, and dual-arm robotic execution within a modular, IoT-enabled infrastructure. DigiHortiRobot is structured into three progressive implementation phases: (i) monitoring and data acquisition through a multimodal perception system; (ii) decision support and virtual simulation for scenario analysis and intervention planning; and (iii) autonomous execution with feedback-based model refinement. The Physical Layer encompasses crops, infrastructure, and a mobile dual-arm robot; the virtual layer incorporates semantic modeling and simulation environments; and the synchronization layer enables continuous bi-directional communication via a nine-tier IoT architecture inspired by FIWARE standards. A robot task assignment algorithm is introduced to support operational autonomy while maintaining human oversight. The system is designed to optimize horticultural workflows such as seeding and harvesting while allowing farmers to interact remotely through cloud-based interfaces. Compared to previous digital agriculture approaches, DigiHortiRobot enables closed-loop coordination among perception, simulation, and action, supporting real-time task adaptation in dynamic environments. Experimental validation in a hydroponic greenhouse confirmed robust performance in both seeding and harvesting operations, achieving over 90% accuracy in localizing target elements and successfully executing planned tasks. The platform thus provides a strong foundation for future research in predictive control, semantic environment modeling, and scalable deployment of autonomous systems for high-value crop production.

Suggested Citation

  • Roemi Fernández & Eduardo Navas & Daniel Rodríguez-Nieto & Alain Antonio Rodríguez-González & Luis Emmi, 2025. "DigiHortiRobot: An AI-Driven Digital Twin Architecture for Hydroponic Greenhouse Horticulture with Dual-Arm Robotic Automation," Future Internet, MDPI, vol. 17(8), pages 1-15, July.
    • Handle: RePEc:gam:jftint:v:17:y:2025:i:8:p:347-:d:1714160
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    References listed on IDEAS

    as
    1. Ruixue Zhang & Huate Zhu & Qinglin Chang & Qirong Mao, 2025. "A Comprehensive Review of Digital Twins Technology in Agriculture," Agriculture, MDPI, vol. 15(9), pages 1-25, April.
    2. Cristian Bua & Luca Borgianni & Davide Adami & Stefano Giordano, 2025. "Reinforcement Learning-Driven Digital Twin for Zero-Delay Communication in Smart Greenhouse Robotics," Agriculture, MDPI, vol. 15(12), pages 1-23, June.
    3. Verdouw, Cor & Tekinerdogan, Bedir & Beulens, Adrie & Wolfert, Sjaak, 2021. "Digital twins in smart farming," Agricultural Systems, Elsevier, vol. 189(C).
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