## 2 Literature Review  
UAVs are identified as one of the technology enablers related to IoT (Internet of Things) systems [15] and towards enhancing the smart farming applications [16]. In recent years, a growing number of studies focus on UAV-based approaches for smart farming, suggesting sustainable agricultural methods [17], enhancing smart farming communications [18], and transforming traditional farming practices [19]. The combination of UAVs and IoT-based approaches is recognized as a new paradigm [20, 21], leading to further research activities and sensory-based approaches to enhanced smart farming. Some studies highlight the UAV-based applications for better communications in agriculture [22], including methods of digitizing agriculture and achieving precision agriculture. As suggested by [23], there is still scope for further development to utilize deep learning, especially when combined with key technological advances such as UAVs. From a much broader understanding, we could see UAVs to be applicable to overcome smart farming challenges; for instance, by using the visible light sensors (RGB sensor) and UACE camera capture [24] to enhance communications in an easy and low-cost way. In other studies, the use of distributed simulation [25] is an effective approach in managing connections and communications, which are then more effective for smart farming practices. In the area of smart family, we see more studies focusing on UAV integration in the IoT or IoIT systems. Such integration is helpful, as it enables us to optimize the IoT solutions [26], provides better data management for smart farming practices [27], and embraces a wide technological development in the field of smart farming [28]. The architecture model, as described by [29], highlights the role of communication technologies and IoT-based platforms, enabling sensory systems to provide \"immediate monitorry and optimisation of crops\". In doing so, we note a range of challenges and shortfalls regarding controls and applications [30], and limitations for multi-sensory systems and technologies, and battery life. Thus, this study aims to address some of these common limitations and help to consider ways that could be used for optimizing the UAV application in current and future smart farming practices. Existing literature already highlights the role of smart farming as a crucial approach to developing sustainable agriculture [31]. More recently, deep reinforcement learning has been used to optimize UAV navigation and to provide better connectivity and communication [32]. This approach has broadened areas of innovation such as modeling and coverage of field areas [33], target tracking [34], and battery management [35]. The cooperation between the management aspect and enhancing the IoT system is useful for UAV-enabled or UAV-based methods, particularly that we could go beyond just the common areas of irrigation, fertilization, and disease and weed detection [36]. An important area is to manage battery life, which could be done by enhancing the navigation and route decision-making of the UAVs. In this study, we cover key aspects of coverage path planning, such as the one identified by [37] and move towards more integrated approaches that include UAVs, IoT, and battery management. The study also highlights the role of reinforcement learning-based approaches in itinerary planning, particularly to be used for smart farming applications and practices. In doing so, the study suggests optimization methods for routing and pathfinding, which will be directly linked to battery life management for the UAVs when used in the agricultural field. UAV itinerary planning in smart farms aims to address four key issues: (1) minimizing journey delay, (2) flight distance reduction, (3) increased UAV resource conservation (mainly power), and (4) maximizing the number of collected data samples. For this, a number of route planning approaches have been proposed to forward UAVs through best-fitted and cost-effective routes.