Contents lists available at ScienceDirect Computers and Electronics in Agriculture journal homepage: www.elsevier.com/locate/compag Original papers Open geospatial infrastructure for data management and analytics in interdisciplinary research Jacob Høxbroe Jeppesen a, ⁎ , Emad Ebeid b , Rune Hylsberg Jacobsen a , Thomas Skjødeberg Toftegaard a a Department of Engineering, Aarhus University, Finlandsgade 22, 8200 Aarhus N., Denmark b Faculty of Engineering, University of Southern Denmark, Campusvej 55, 5230 Odense M., Denmark ARTICLE INFO Keywords: Internet of Things Remote sensing Open software Open data Farm management information systems ABSTRACT The terms Internet of Things and Big Data are currently subject to much attention, though the specific impact of these terms in our practical lives are difficult to apprehend. Data-driven approaches do lead to new possibilities, and significant improvements within a broad range of domains can be achieved through a cloud-based infra- structure. In the agricultural sector, data-driven precision agriculture shows great potential in facilitating the increase in food production demanded by the increasing world population. However, the adoption rate of precision agriculture technology has been slow, and information and communications technology needed to promote the implementation of precision agriculture is limited by proprietary integrations and non-standardized data formats and connections. In this paper, an open geospatial data infrastructure is presented, based on standards defined by the Open Geospatial Consortium (OGC). The emphasis in the design was on improved interoperability, with the capability of using sensors, performing cloud processing, carrying out regional sta- tistics, and provide seamless connectivity to machine terminals. The infrastructure was implemented through open source software, and was complemented by open data from governmental offices along with ESA satellite imagery. Four use cases are presented, covering analysis of nearly 50 000 crop fields and providing seamless interaction with an emulated machine terminal. They act to showcase both for how the infrastructure enables modularity and interoperability, and for the new possibilities which arise from this new approach to data within the agricultural domain. 1. Introduction The Internet of Things (IoT) and Big Data have gained tremendous attention in recent years, and we are currently witnessing information and knowledge from data becoming a critically important tradable asset. Most domains are becoming increasingly data-centric, however, it is difficult to gain an overview of the specific benefits one might achieve from this. Furthermore, interoperability has, and still is, an issue, and standardization is necessary for achieving modularity, where a broad range of software and hardware modules can be seamlessly connected. This calls for a standardized cloud-based infrastructure with an eco-system approach, resulting in third-party vendors becoming able to develop add-ons to existing systems, much like apps for smartphones. The agricultural sector is required to increase yield production to meet the expected doubling of crop demand from 2005 to 2050 (Tilman et al., 2011). Meanwhile, 70% of freshwater withdrawals are already devoted to irrigation and the agricultural sector is responsible for 30–35% of greenhouse gas emissions (Foley et al., 2011). This is partly caused by 50% of the global nitrogen applied not being absorbed by the crops they were distributed on (Stuart et al., 2014). Hence, there is a critical need for new methods for optimizing the agricultural domain. Precision agriculture employs technologies to manage spatial and temporal variability within fields to improve crop yield while de- creasing the environmental impact. Hence, it facilitates site specific farming, such that e.g. irrigation and nitrogen fertilizer is distributed only where and when necessary. However, the adoption rate of preci- sion agriculture has been slow, due to a range of factors, such as issues with interoperability, compatibility, and complexity (Aubert et al., 2012). Recent advances in open source software, open standards, and open interfaces show potential for overcoming these issues. The standardi- zation of interfaces and formats by The Open Geospatial Consortium (OGC) can ensure interoperability of geographic information systems (GIS) (Nash et al., 2009; Nikkilä et al., 2010), which is an essential part https://doi.org/10.1016/j.compag.2017.12.026 Received 6 July 2017; Received in revised form 8 November 2017; Accepted 18 December 2017 ⁎ Corresponding author. E-mail address: jhj@eng.au.dk (J.H. Jeppesen). Computers and Electronics in Agriculture 145 (2018) 130–141 0168-1699/ © 2017 Elsevier B.V. All rights reserved. T