## 4 Conclusions  
Agricultural robots will be applied in farming tasks, both outdoors and inside greenhouses. This paper aims at integrating their management and with the possibility of exploiting them for additional services, e.g., to provide complete and detailed climate mapping of the environment, which is particularly important to ensure and improve the health and growth of plants. We propose a novel solution for the identification of the best trajectory to be performed by an agricultural robot with the aim of adequately balancing measurement accuracy and energy autonomy. This method is based on the application of reinforcement learning together with the Proximal Policy Optimization (PPO) algorithm for the reward function, which takes into consideration both the importance of each PoI in the accuracy of the greenhouse climate mapping and the need to recharge the robot when its battery goes below a given threshold level. For doing so, the environment has been formalized through a labeled bi-directional graph, in which nodes represent Pols where measurements and recharge can take place, and edges are the possible paths between them. We also formalized the technological features of the robot, i.e., the speed and the consumption rate. This representation, together with the usage of historical data to train the PPO algorithm, represents an original example of a mixed model-driven and data-driven approach in the field of precision agriculture. More features related to the environment and the robot could be added in the future. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**PoI**} & \multicolumn{4}{c}{**Proposed Work**} & \multicolumn{4}{c}{**Previous Work [18]**} & \multicolumn{4}{c}{**Baseline Method [17]**} \\ \cline{2-13}  & **\# Visits** & **MAPE\%** & **MAE** & **MSE** & **\# Visits** & **MAPE\%** & **MAE** & **MSE** & **\# Visits** & **MAPE\%** & **MAE** & **MSE** \\ \hline
1 & 419 & 5.1 & 0.93 & 2.2 & 343 & 7.7 & 1.3 & 4.4 & 560 & 8.8 & 1.4 & 4.1 \\
2 & 395 & 5.5 & 1.0 & 2.5 & 241 & 5.7 & 1.0 & 2.6 & 560 & 6.1 & 1.2 & 2.6 \\
3 & 396 & 5.6 & 1.0 & 2.6 & 323 & 7.2 & 1.3 & 4.6 & 520 & 8.6 & 1.5 & 4.7 \\
4 & 356 & 5.9 & 1.2 & 3.2 & 53 & 7.3 & 1.3 & 4.6 & 560 & 8.3 & 1.5 & 4.8 \\
5 & 383 & 5.2 & 1.0 & 2.6 & 62 & 6.8 & 1.3 & 3.6 & 520 & 7.5 & 1.4 & 3.8 \\
6 & 384 & 5.8 & 1.2 & 3.7 & 87 & 8.2 & 1.6 & 6.0 & 560 & 8.3 & 1.6 & 5.3 \\
7 & 405 & 5.1 & 1.0 & 2.5 & 161 & 6.8 & 1.3 & 4.7 & 560 & 6.9 & 1.3 & 3.7 \\ \hline All & 2738 & 5.4 & 1.1 & 2.7 & 1270 & 7.1 & 1.3 & 4.3 & 3840 & 7.8 & 1.4 & 4.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison between the estimation errors of the model in [15] when it is trained with the trajectories produced using: (i) the proposed approach, (ii) the previous approach in [18], (iii) the continuous complete itinerary used in [17]. Climate monitoring deserves particular attention. This process, being data-driven, is not statically predefined; thus, it represents an example of _active sensing_. Furthermore, if we assume that temperature evolution follows a hidden physical behavior, its continuous observation to predict future values can be considered as an attempt to build its model, which then is used to decide the robot's actions, thus fully recalling the concept of _digital twin_. Future work may consider more complex and interesting dynamic models, such as those of plants and pathogens, so that the decision of the robot's actions could be not only restricted to trajectory planning but also to full agronomic tasks. The proposed approach was tested using data for a real-world scenario regarding a greenhouse in Verona, a city in Northeast Italy. The obtained results were compared with two baselines in [17; 18], demonstrating the goodness of the proposed approach. In particular, the production of the training set was improved, since it allows the prediction model to produce temperature estimations that were, overall, about 2-2.5% more accurate compared to the ones obtained with the training sets produced by the two baselines.