# Augmented Reality in Precision Farming: Concepts and Applications
## 1 Introduction  
Augmented Reality (AR) applications have become prevalent within smart industry manufacturing [1] and wider popular culture sectors over the last decade [2]. This is largely due to the cumulative accessibility of game engine-based Software Development Kits (SDK) (e.g., Vuforia [3], Aryzon [4], Zappar [5]) and the affordability of hardware devices for content display. Several libraries (e.g., ARjs [6]) now also cater for web-based development, providing centrally hosted AR deployment without the end-users required to install software on their own devices. AR is now firmly established within many smart city application domains, with smart production line engineering [7], preventative maintenance [8] and education-based applications in particular benefitting from the lower barrier of entry for AR development. Within agriculture-based research, digital visualisation technologies are contributing to the betterment of both precision livestock [9] and crop farming by increasing efficiency and reducing supervisory costs [10]. It is within this domain that AR finds an essential use in an agricultural setting. The reason AR is developing a particular role, more so than other related optical technologies such as Virtual Reality (VR), is that the physical world is enhanced by the use of 3D assets or projected data insights, meaning the user's interaction with the coronal environment is not inhibited but rather upgraded through an extension of the reality. For example, as [PERSON] et al. demonstrate, AR has the ability to provide a way-finding process to guide farmers during traditionally laborious soilsampling processes [11]. Gains are also achieved by overlaying visualisations of simulated crop growth models [12], and the projection of data into real-world objects for real-time decision support, such as insect or disease species identification. In all examples, the virtual assets seamlessly co-exist with the visual perception of the real world [13]. It is clear that AR is displaying potential for the enrichment of both the crop and livestock management processes within a precision farming setting [10]. However, even though many works specify the benefits AR offers within agriculture [11, 14, 15, 16], it must be emphasised that the advantages of AR for precision farming are reliant on a symbiotic relationship with other core smart city-based technologies, such as machine learning or GPS integration, to provide a cite-specific management service. As evidence, consideration is drawn from the aforementioned approach by [PERSON] et al., where the benefits of AR for use in soil sampling are only made possible by coupling the technology with Internet of Things (IoT) data [11]. This is similarly the case for the crop growth monitoring and real-time decision processes that are outlined in the work by [PERSON] et al. [12]. Both the AR software and hardware constraints have unique requirements when used within an agriculture-based setting compared to the use in other sectors, such as entertainment or manufacturing. The role of the farmer is practical in nature, meaning that AR users will tend to work long hours and in remote locations [17]. Therefore, head-mounted displays or hand-held devices should be lightweight, weather resistant, not restrict movement and capable of providing multiple applications from the same hardware. This is confirmed by [PERSON] et al. [16], who discuss that smart glasses tend to be the appropriate route forwards for deployment within livestock-based precision farming applications. The lightweight and unrestrictive nature of the hardware means that the technology can be used for prolonged periods without impacting movement. Additionally, it is possible to further sub-divide the required functionality challenges of AR between the separate domains of crop and livestock farming processes. For example, the task of catering for large geographic areas, such as pastures within dairy farms where, as [PERSON] et al.