## 1 Introduction
### Related Work  
As mentioned, wireless sensor networks (WSNs) are a useful framework that allows farmers and researchers to monitor real-time data precisely. This possibility could be beneficial for farmers to manage the required resources of their fields and improve their crop yields [19]. In [20], a dense WSN was used for greenhouse climate control. Simulations on monitoring and controlling greenhouse climate using WSNs were conducted in [14]. Building on this, in [21], the authors studied the application of WSNs for controlling greenhouse parameters in precision agriculture. In 2015, a study conducted in a Greek greenhouse indicated the capabilities of WSNs in achieving spatially distributed climate control. In this regard, [22] proposed a hierarchical WSN system for greenhouse monitoring, combining Internet technology and intelligent object communication models.  
As highlighted in the introduction, despite the undeniable success of WSNs in greenhouses, they suffer from various shortcomings in different aspects, which leads to increasing interest in virtual sensors. As indicated in [23], the application of virtual sensors in greenhouses is limited but includes many different attempts [24; 25; 26; 27].  
In general, virtual sensing approaches can be divided into two main categories: _model-based_ and _data-driven_ methods [28]. Model-based virtual sensors exploit system knowledge described through equations [29], while data-driven virtual sensors rely on historical measurements, which serve as input for statistical or machine learning methods [30; 31]. A typical example of a model-driven approach is represented by the use of computational fluid dynamics (CFDs) rules that model fluid flows inside the greenhouses [32; 33; 34]. Studies in this area have concentrated on CFD simulations for analyzing experimental data [35], forecasting temperature distribution [36], creating dynamic models for greenhouses [37], and investigating thermal balance in greenhouse air conditioning systems [38]. Recent advancements include the integration of machine learning with CFD to improve accuracy and efficiency in fluid flow simulations [39] and the development of virtual sensor systems for real-time monitoring of greenhouse parameters like temperature, humidity, and CO\({}_{2}\)[40]. However, this approach remains challenging due to its complexity, high computational requirements, and sensitivity to disturbances in greenhouse environments [34].  
Unlike the model-based approach, data-driven virtual sensors estimate physical conditions using available data, statistical methods, and state-of-the-art machine learning techniques. For instance, the relationship between temporary and permanent sensors, considering both time and spatial context, is captured by linear regression in [41] and  
Figure 3: Example of enriched model environment where some PoIs have been identified as charging stations and some direct paths have been identified.  
by a trained recurrent neural network (RNN) in [15]. The method presented in [42] uses reinforcement learning (RL) to manage sensor states. In this method, the agent learns from LSTM-based predictions to determine the best times to activate or deactivate sensors, aiming to save energy while maintaining accuracy.