## 1 Introduction  
The COVID-19 pandemic has underscored the critical role of digital technologies in sustaining and enhancing operations across sectors. Among them, agriculture, an essential pillar of the primary economy, stands to benefit significantly from this digital transition. Embracing emerging technologies is likely to prove pivotal for improving competitiveness, addressing structural inefficiencies, and mitigating risks associated with future food supply crises. Simultaneously, demographic projections by the United Nations anticipate a global population of 8.5 billion by 2030 and approximately 9.7 billion by 2050 [1]. These projections intensify the demand for sustainable agricultural intensification, particularly under constraints such as declining arable land, labor scarcity, and the low profitability of small- to medium-scale farms. To respond to these challenges, researchers have proposed a wide range of innovative approaches. Controlled environment agriculture systems, such as hydroponics and aeroponics, have demonstrated the potential to reduce water usage, eliminate pesticide dependence, and lower greenhouse gas emissions [2; 3]. In parallel, the integration of sensory systems with intelligent analytics and machine learning has enabled enhanced crop monitoring, precision interventions, and yield optimization [4]. Recently, explainable AI approaches have also been applied to greenhouse decision-making; for example, [PERSON] et al. (2025) demonstrated the use of interpretable machine learning models for irrigation scheduling and yield prediction in controlled environments [5]. Additionally, robotics platforms equipped with advanced perception and learning algorithms have proven effective in automating repetitive and labor-intensive horticultural tasks [6]. Despite these technological advancements, real-world deployments remain fragmented, with limited interoperability between sensing, control, and actuation components. Unlocking the full potential of smart farming technologies requires integrative frameworks that bridge the physical and digital domains. In this context, digital twins offer a promising paradigm for enabling seamless synchronization, predictive intelligence, and closed-loop control in precision agriculture. This article introduces DigiHortiRobot, a novel digital twin architecture designed for hydroponic greenhouse horticulture using a dual-arm mobile robotic platform. The system integrates sensing, control, predictive modeling, and autonomous task execution within an IoT-enabled infrastructure. DigiHortiRobot provides the following:  
* Real-time monitoring of crop status, environmental variables (e.g., CO\({}_{2}\), temperature, humidity, pH), and robotic actions;
* Simulation-based inference for seeding and harvesting optimization;
* Self-diagnosis and predictive maintenance based on robot telemetry;
* Closed-loop refinement of decision policies via interaction-aware feedback. DigiHortiRobot explicitly places the farmer at the center of the decision loop, enabling remote monitoring, scenario simulation, planning, and validation via a virtual representation of the greenhouse. By integrating robotics, artificial intelligence, Big Data analytics, and cloud computing, DigiHortiRobot offers a robust framework for sustainable, high-efficiency greenhouse horticulture. The main contributions of this article are as follows: (i) the design of a unified and modular digital twin architecture tailored for smart greenhouses; (ii) the implementation of AI-driven decision-making layers within the digital twin framework; and (iii) a demonstration of a three-phase execution model encompassing monitoring, virtual planning, and physical deployment for key horticultural tasks. Furthermore, the proposed system introduces a unified virtualization approach capable of representing biological, infrastructural, and robotic elements, overcoming current limitations in agricultural digital twin prototypes. The rest of the article is organized as follows.