Reinforcement Learning-Driven Digital Twin for Zero-Delay Communication in Smart Greenhouse Robotics

Cristian Bua, Luca Borgianni, Davide Adami and Stefano Giordano ()
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Cristian Bua: Department of Information Engineering, University of Pisa, Via G. Caruso, 16, 56122 Pisa, Italy
Luca Borgianni: Department of Information Engineering, University of Pisa, Via G. Caruso, 16, 56122 Pisa, Italy
Davide Adami: Department of Information Engineering, CNIT—University of Pisa, Via G. Caruso, 16, 56122 Pisa, Italy
Stefano Giordano: Department of Information Engineering, University of Pisa, Via G. Caruso, 16, 56122 Pisa, Italy

Agriculture, 2025, vol. 15, issue 12, 1-23

Abstract: This study presents a networked cyber-physical architecture that integrates a Reinforcement Learning-based Digital Twin (DT) to enable zero-delay interaction between physical and digital components in smart agriculture. The proposed system allows real-time remote control of a robotic arm inside a hydroponic greenhouse, using a sensor-equipped Wearable Glove (SWG) for hand motion capture. The DT operates in three coordinated modes: Real2Digital, Digital2Real, and Digital2Digital, supporting bidirectional synchronization and predictive simulation. A core innovation lies in the use of a Reinforcement Learning model to anticipate hand motions, thereby compensating for network latency and enhancing the responsiveness of the virtual–physical interaction. The architecture was experimentally validated through a detailed communication delay analysis, covering sensing, data processing, network transmission, and 3D rendering. While results confirm the system’s effectiveness under typical conditions, performance may vary under unstable network scenarios. This work represents a promising step toward real-time adaptive DTs in complex smart greenhouse environments.

Keywords: digital twin; zero-delay network; negative delay; remote control; wearable device; smart agriculture; reinforcement learning (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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