## 1 Introduction  
Remote sensing applications aim to remotely capture and report environmental data samples using sensory devices for further processing and/or decision making. Robust, fast, and accurate data collection plays a critical role in agriculture remote sensing applications. This means farming and/or natural resources are threatened/wasted if late (out-of-date), meaningless and/or inaccurate sensory recordings are reported [1]. For example, farm animals fall into danger and/or are threatened by environmental risks if they are not continuously monitored. According to [2], there are two paradigms to address remote sensing applications: client/server and mobile agent (MA). The former deploys a remote sensing infrastructure to collect and forward environmental data samples through Zigbee, Bluetooth and/or internet links (e.g., 5G). In contract, the latter forwards a mobile object (e.g., drones) for sensory data collection throughout a farm. However, MA remote sensing increases the deployment cost and risk, especially in large and wide areas such as farms [3]. Unmanned Aircraft Vehicles (UAVs) are increasingly used for environmental remote sensing in urban and rural areas such as farms [4]. There are a number of UAV remote sensing applications such as animal behavior monitoring, farm surveying, and herd tracking [5]. UAVs are usually equipped with sensory devices--mainly cameras to collect and report high-resolution environmental data samples. They utilize GPS information on-the-fly and move without a human pilot on-board. However, UAVs can be navigated by users using remote-controllers. UAV path planning aims to form itineraries to forward either a single or multiple UAVs to do a particular mission such as remote sensing. Path planning algorithms usually suffer from restricted power (and bandwidth) resources, which provide them short traveling distance and limited communication and computation capacities [6]. Due to this, remote sensing UAVs should fly through best-fitted and cost-effective routes to avoid overlapped, duplicated, irrelevant and/or out-of-interest data collection [7]. Yet, they need to utilize efficient path planning approaches aiming to minimize the total flight length and avoid random/blind movement [4]. However, UAVs should avoid performing complex computing algorithms (i.e., path planning and remote sensing computation/aggregation) and/or frequent wireless communication (i.e., network interconnections and flight synchronizations/navigations) to conserve battery resources [8]. Hence, there is still a major gap in the usage/utilization of UAVs in some practices, such as in smart farming. This papers aims to respond to this specific matter. UAV paths are usually planned according to two categorizes: proactive and reactive [9]. Proactive path planning provides routes in advance. This is a fixed route plan and supports no update during the flight. By this, proactive routing fits no dynamic remote sensing application--mainly mobile object tracking. Reactive routing dynamically establishes the UAV's routes on-the-fly according to the updating mission or changing environment. However, this may result in increased route planning overhead and enhanced UAV power consumption, especially if the mission/environment is frequently or continuously updated. Machine learning techniques offer UAV path planning a number of benefits--mainly autonomous and reactive routing in remote sensing application [10]. Reinforcement learning (RL) [11] is a machine learning technique that has the potential to be commonly used for real-time decision-making problems such as UAV route planning. By this, UAVs are able to explore the environment and learn the best-fitted and/or cost-effective paths to the target areas for sensory data collection. Indeed, RL-enabled UAV route planning provides a trial-and-error environment interaction paradigm to figure out optimal routes to fly through. Q-learning [12] is a model-free and value-based algorithm of reinforcement learning that works according to an action-reward approach.