# Augmented Reality in Precision Farming: Concepts and Applications
## 4 Findings
### Further Reflection and Validity  
Based on the findings, this sub-section reflects on precision farming and AR in a wider (global) context. To address some of the world's current environmental, economic and social challenges, the United Nations put forth the Sustainable Development Goals (SDGs) [39]. The 2030 agenda completion has been a significant topic for the scientific community over the past years. From the SDGs implementation there are two key lessons. Firstly, scarcity of reliable data can frustrate a country's ability to optimize investment, create policy, make decisions and measure progress. Secondly, even though significant progress has been made, such progress has been unequal amongst countries and areas. Some of those inequities are attributed, amongst other things, to different technological capacities and data availability [40]. Within the agricultural domain, this is especially relevant for Goal 2 which aims to end hunger, improve achieve food security and nutrition whilst promoting sustainable agriculture; particularly target 2a that focuses on increasing investment through an enhancement of international cooperation opportunities, namely in rural infrastructure and agricultural research. Furthermore, 2a emphasises technology development to enhance agricultural productive capacity in developing countries. As agriculture systems around the world strive to become more productive and less wasteful, in other words yield more by using smart city technologies with less input, sustainable practices must be pursued from a holistic perspective and integrated perspective [41], which directly links to the purpose of precision farming. In the SDGs documents it is regularly highlighted that data influx should be time-driven and available for decision making, this is one of the arguments used to advocate the importance of novel technological approaches, like earth observation products [42]. One could argue immersive technologies fall within these novel technological approaches and therefore could be of interest for SDG monitoring workflows. Literature testing the application of AR technology in agriculture within the SDG context (e.g., SLR-based methodologies) is to the best of our knowledge not yet available due to the limited volume of published works of AR in Agriculture from 2016-2021. However, other domains, for example education have found advantages in \begin{table}
\begin{tabular}{c c c} \hline \hline
**Article** & **Agriculture Domain** & **Medium \({}^{*}\)** \\ \hline [PERSON] et al. [15] & Crop & Optical see-through HMD \\ [PERSON] et al. [12] & Crop & Video see-through \\ [PERSON] et al. [31] & Crop & Video see-through \\ [PERSON] et al. [26] & Crop & Video see-through \\ [PERSON] et al. [10] & Crop & Monitor-based \\ [PERSON] et al. [13] & Livestock & Video see-through \\ [PERSON] et al. [16] & Livestock & Optical see-through HMD \\ \hline \hline \end{tabular} \({}^{*}\)In some articles, as the hardware model is not implicitly discussed, the authors infer the actual hardware model or list as Unknown when not evident.  
\end{table} 
Table 2: AR Hardware Approaches \({}^{*}\). the use of AR to meet their corresponding SDG [43, 44], which may signify an impending growth in the uptake of AR in farming the near future. Another notable aspect for succeeding in SDG monitoring is the way in which data is communicated. Information and monitoring results should be adequate for their application. Moreover, the results should be interpretable by different stakeholders. Since SDGs are often broad, complex and large-scale, it is idyllic to find a way to show results locally (i.e., what does it mean for the farmer?) in a live, data-driven, format. In that sense, immersive technologies, such as AR, could speed-up information delivery and retrieval for decision making, ultimately aiding in the process of achieving SDGs monitoring and evaluation.