# Augmented Reality in Precision Farming: Concepts and Applications
## 2 Augmented Reality in Agriculture
### AR in for Crop and Livestock Management  
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 [PERSON] 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. [PERSON] et al., present the use of AR for the supervision of two autonomous agricultural machines [15] for sowing, spraying or harvesting. In this setting, AR provides the farmer with situational awareness, relaying operational information, such as the directional movement and fault status. There are, of course, other solutions which can provide this information to the farmer (e.g., data dashboards, mobile applications), however the use of AR means that the users' focus on the real physical world can be sustained when viewing the data. In this specific example, 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 the aforementioned research presented by [PERSON] et al. [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. This is demonstrated by [PERSON] et al., who discuss an AR-based application for crop disease detection to support farmers in India. 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; as demonstrated by [PERSON] et al., who work with ear-tag sensors and machine intelligence for the remote behavioural trail analysis of pigs [27]. In other words, 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]. At the same time, the number of farms is decreasing and the farmer population is older on average. For example in the United States, the average farmer age is 57 [29] while in Europe, 7 out of 10 farmers are 55 years or older [30].