The basis of the precision farming concept within crop management, is the achievement of a higher production using fewer resources through technological enhancements, by supporting management spatially and temporally through industry 4.0 technologies [21,24]. This may include working with existing equipment, such as human-driven machines, but adding remote sensing or data collection methods that provide a data-driven insight, unlocking an optimisation of resources; or a layer of automation [9]. More advanced smart city technological solutions, such as drones, and digital twin technologies further enhance solutions, providing autonomous services using intelligent data insights; with a comprehensive range, including yield mapping, parallel running of machinery and automated soil sampling, provided by Klepacki et al. [25]. However, there are additional added values aside from increased agricultural output, namely better operational economics, reliability and general insight into the systematic operation of the farm as a whole. Notably, as demonstrated in this article, AR has the flexibility to be deployed comfortably within each precision farming setting. By coupling with existing equipment, in parallel with the diverse industry 4.0 applications, and integrated into new autonomous machine solutions, AR is able to synergise virtual objects with the existing physical real world. This makes the technology particularly suited to practical crop farming, as discussed in the following examples.

Yet, in order for AR to fulfil a data visualisation function, integration is required with other services; namely a platform capable of autonomously controlling machines when in the field. This is also the case in [12], where AR provides a visual outlet for the integration of other industry 4.0 digital solutions such as sensors, network infrastructure, GPRS, Wi-Fi, compass, crop information, a decision layer for crop image detection and database matching technological services [12,26].

Within crop farming, AR’s adaptability suits a wide spread distribution model. Relevant and on time information is a powerful tool for farmers, even more so when the data is provided as an integration with the real-world. For instance, smart phones, which have become a highly accessible technology (provide network access when working in remote locations), mean that widespread deployment is possible. AR can be positioned successfully, where more immersive-heavy solutions, such as VR, may not prevail.

Regarding livestock management, precision farming principles are based on the production and management of animals, driven through sensors and data services. In this setting, precision livestock farming concerns the implementation of technologies to enable real-time monitoring for a per-animal approach. Demand and consumption of animal products are projected to increase in the coming years [28]. This results in less manpower to manage larger numbers of animals, making individual management and subject identification a difficult task. Implementation of technological tools, such as radio frequency identification devices (RFID) and livestock monitoring systems (LMS), offer solutions for this per animal identification and management concept [13,16]. However, the provided implementation is limited without a visual or interactive outlet for the information. Yet, similarly to the crop management applications, AR opens up the possibilities for a real-time data-driven display of individual animals in the farmers view, both in dynamic scenarios (i.e., grazing cows) and more static scenarios (i.e., lactating cows). For example, Maria et al. [16] showcase the advantages of using AR through smart glasses to retrieve information (feeding, milking, breeding, health etc.) per subject in real-time. The technology provides a window to the remote sensor data on a per animal approach level. A further example is presented by Zhao et al., who developed a mobile application to locate (track) and manage large numbers of cattle in extensive areas [13]. The application combines GPS measurements and computer vision algorithms to deliver information of cow locations in the field.

AR has flexibility within different farming contexts. While it is clear that AR is employed as means to communicate information visually, it is the integration of AR with other technologies that enables the tailored functionality for precision farming and the visual-based modality [10,31] Coupled technologies used are diverse, but core commonalities are present; such as the use of on-line databases, machine learning algorithms and sensor/IoT connectivity (i.e., humidity, temperature and weather sensors). The choice of technologies can be described as dependent on the context and domain in which the application will be used, with both foundational differences and overlaps between the two application areas of crop farming and livestock management.

Whilst each AR application addresses a specific need (often unique to its deployment context) resulting in aspects that might be most notable for one domain but not be relevant for the other, AR technologies do share common requirements. For example, the use of QR codes to facilitate management tasks in environments where a low region of movement in space is expected; for example for indoor dairy farming or greenhouse management [16]. However, when dealing with a more dynamic scenario, for instance livestock dispersed over a wide area or large-field crop management, synergies between the AR deployment may include the combination of GPS and computer vision algorithms, as demonstrated by [13].

Yet a notable consideration is apparent in all deployment scenarios, and that is that location is key. Whether it is a local coordinate system enabled by cameras or a geographic coordinate system using GPS sensors [15], or a hybrid approach [13], applications are only able to properly convey information through AR with an appropriate level of location awareness and suitable coupling technologies to enable the process.

The type of coupled technology provides insights into the nature of the application for precision farming. In that regard, it is possible to classify the applications into specific AR types. Edwards-Stewart et al., for example, categorize AR applications based on their functional characteristics and propose two main categories, (A) triggered and (B) view-based AR applications [32]. Triggered AR applications, which as the name suggests, are AR approaches that need to be triggered by a stimuli to deliver the augmentation of reality. In contrast, view-based applications can augment content without the need of a reference in the view.

Triggered applications can be of four types: (1) marker-based, (2) marker-less (location-based), (3) dynamic augmentation and (4) complex augmentation. Marker-based involves the use of a QR code pattern to initiate the AR projection. Marker-less does not require a QR code, and in some instance the user would be required to tap on screen to create the projection on their desired location. The latter two types (dynamic and complex augmentation) highlight specific types of AR marker-less applications. Figure 1 displays an overview of the reviewed works in Section 2.1 divided by AR type, based on the four classifications in [32], to provide an example of the triggered applications currently deployed within an agriculture setting.

Coupled technologies are facilitators for AR. For example, without GPS to facilitate location tracking [15], it would not be possible to create a marker-less AR approach. Similarly, without the combination of more than one coupled technology, higher location accuracy might be unattainable impacting the success of [10,12,14,16,26]. For marker-less applications, position is often not the only factor involved in the correct overlay of data. For example, the multirobot system developed by Huuskonen et al., requires information on the orientation of the AR headset used by the driver to correctly overlay objects in the real world [11,15]. The accuracy of the internal headset orientation sensor was, however, not sufficient for this application; therefore, an extra sensor is introduced into the framework.