## 6 Conclusions and Future Work  
This paper utilizes the reinforcement learning technique (Q-learning) in UAV path planning for smart farm remote sensing. This addresses an autonomous UAV itinerary  
Figure 8: The average traversed distance (based on synthesized data of the model generated for this study). planning, which is established based on the Q-learning exploration-exploitation paradigm. Under this study, a single UAV is programmed to autonomously fly, discover the environment (autonomous zone-based labelling) and record the sensory points (SPs) with minimum cost/delay and maximum accuracy. Yet, this UAV collects, aggregates, and reports the sensory recordings to the base station for the end-user further processing and/or decision making. The performance of Q-leaning enabled UAV routing is compared with two benchmarks, including IEMF and TBID. This allows us to study the superiority and efficiency of reinforcement learning and environment exploration techniques compared to infrastructure-based (e.g., tree) and/or heuristic UAV route planning approaches. According to the results, Q-learning enabled UAV remote sensing offers a number of benefits, including reduced energy consumption, ETE, and traversed distance, and an enhanced number of captured sensory recordings, especially if SPs are crowded and dense. This shows that Q-learning allows us to find the best-fitted (e.g., minimized ETE and distance) and cost-effective (e.g., power consumption) paths to visit SPs and capture sensory recordings. However, infrastructure-based UAV route planning approaches reduce ETE and traversed distance, although they address increased power consumption required to establish the infrastructure. The energy consumption is significantly increased in infrastructure-based UAV route planning if SPs are highly mobile and the infrastructure needs to be repeatedly updated. Yet, heuristic UAV path planning increases power consumption, ETE, and traversed distance as it forwards UAV according to greedy algorithm. According to the simulation results, this addresses wrong flight directions and leaves remote sensing missions incomplete because the IEMF enabled UAV fails to find the next visiting SP when SPs are rare. This study responds to the existing research gap on the use of UAV for smart farming. It models and addresses optimizing itinerary planning, path finding, and battery life management. The findings are novel, as the study uses reinforcement learning technique to autonomously partition a farm and dynamically forward UAVs through minimum distance and power composition routes for non-overlapped data collection. In doing so, the study feeds into future research work that aims to enhance the use and integration of UAV use in smart farming practices. Further investigation on Q-learning UAV path planning optimization is still required. For this, the award function may consider a set of other features such as data-type to address data-centric UAV remote sensing. By this, the UAV can be programmed to fly over a farm and capture particular sensory recordings according to the user interest and/or remote sensing requirement. Moreover, tuned reinforcement learning algorithms, such as Deep Q Network, Proximal Policy Optimization and meta-reinforcement learning, can enhance path planning performance. Future research studies need to consider the effects of the relation of the field's dimensions on the suggested algorithm. While this study uses a simplified model, future studies could benefit from utilizing this study's findings to explore a more realistic situation, such as irregularities of the field shape, field size, and target areas of the field. Conceptualization, S.P.A. and A.C.; methodology, S.P.A.; software, S.P.A.; validation, S.P.A. and A.C.; formal analysis, S.P.A.; investigation, S.P.A. and A.C.; resources, S.P.A. and A.C.; visualization, S.P.A.; project administration, S.P.A.; funding acquisition, S.P.A. All authors have read and agreed to the published version of the manuscript.