## 1 Introduction  
Greenhouses are typically controlled environments, but considering uniform climate conditions inside them is unrealistic and sometimes harmful for plants health and growth [1]. This fact was originally discussed in the late 1990s, with a focus on humidity and transpiration [2]. Similarly, in [3], the authors revealed the presence of different micro-climates around plants. Starting from these works, other studies like [4, 5, 6, 7, 8, 9] confirmed that relying on spatially accurate information about the micro-climate inside a greenhouse is an extremely important but challenging task. In this domain, technology provides the possibility of measuring and controlling climate variation within the greenhouse. For instance, today, there is a wide range of sensors to monitor different parameters, including air temperature and humidity [10], soil moisture [11], soil irrigation volume, light intensity,and carbon dioxide concentration [12]. However, a significant level of spatial accuracy typically comes with costs regarding the number of sensors to be installed inside the greenhouse. In addition, physical and technological installation constraints must be addressed, which include power supplies, battery and charging of sensor networks, periodic calibration and synchronization, sensor maintenance, avoiding risk of sensor damage in high-temperature and humidity environments, and the impossibility of installing a physical device at each point of interest [13]. The so-called _virtual sensors_, also referred to as _soft sensors_, represent a viable and cost-effective solution to these issues. A virtual sensor is essentially a software model that surrogates a hardware device to provide real-time information at specific points of interest [14]. In [15], a Recurrent Neural Network (RNN)-based technique is presented for constructing virtual sensors trained with data previously collected through temporary sensors inside the greenhouse. In this case, introducing several contextual dimensions of analysis allows for an effective model to predict climatic conditions in several greenhouse locations. Figure 1 illustrates an example of this approach, where seven temporary sensors have been placed in specific points of interest (denoted as _poi\({}_{i}\)_) of the greenhouse (e.g., close to particular plant rows) together with a permanent sensor _cs_, positioned at the center. Each temporary sensor is used for a limited period to collect enough data to understand the relation between the temperature at that point in different time slots, periods of the year, weather conditions, and the measurements provided by _cs_. The corresponding virtual sensors _vs\({}_{i}\)_ can be created for each point of interest as a trained RNN model to replace physical sensors effectively [16]. A more efficient solution is the substitution of the temporary sensors with an agricultural robot that follows a predefined trajectory in the greenhouse to perform its farming tasks [17]. As shown in Figure 2, the robot stops at each PoI during every complete round and regularly takes a measurement in that location. Despite the merits of such solutions, as highlighted in [18], the strategy of periodically stopping in all PoIs is not always the best one. In certain periods, some PoIs could be more interesting than others; thus, in those periods, it could be more preferable to stop more frequently in those PoIs and only occasionally in others. Moreover, the set of interesting PoIs can change over time based on the context or situation of interest in a dynamic way that can be hardly addressed by traditional operational research approaches. Considering all of these, in [18], we start to investigate the problem of identifying the best stopping pattern for the robot based on the contextual importance of each PoI in the _current_ measurement process.