Edward Nash

Applications of open geospatial web services in precision agriculture: a review

Precision agriculture requires the collection, storage, sharing and analysis of large quantities of spatially referenced data. For this data to be effectively used, it must be transferred between different hardware, software and organisations. These data flows currently present a hurdle to uptake of precision agriculture as the multitude of data models, formats, interfaces and reference systems in use result in incompatibilities. This paper presents work on applying standards from the Open Geospatial Consortium and related initiatives to automate agricultural data processing. The selected use-cases demonstrate how such standards may be used to improve the inter-operability of data and software in precision agriculture.

Spatial inference with an interchangeable rule format

Rule interchange between information systems is expanding as new interoperable rule formats are emerging from research. However, existing spatial inference systems generally operate on locally stored data with an internal rule format. Consequently, their design offers little support or facilities for rule interchange. This article presents the requirements, components and design for a spatial inference system with rule interchange. Computational efficiency and overall functionality of the design are considered separately, with the latter demonstrated using encoded agricultural legislation and data. A spatial inference system with rule interchange is based on three primary components: rule representation, spatial functionality and data integration. Of these, the interoperable rule representation and data integration distinctly differ from existing spatial inference systems. The presented inference system combines a spatial superset of the W3C Rule Interchange Format (RIF) with full Open Geospatial Consortium simple feature access (OGC SFA) functionality and on-demand data integration utilising Resource Deception Framework (RDF). The design was found to be effective with a computational efficiency depending predominantly on the spatial operations. This design could be further adapted to implement spatial extensions for existing inference systems. Considerable benefits were also discovered when RIF was used as the native language for the inference engine, thereby removing the need for rule transformations and facilitating on-demand data integration with the GML.

GIS in Agriculture

Since the beginning of the 1990s, modern agriculture and farming has changed dramatically. Agriculture has become a high-tech industry. With the possibility for locating agricultural machinery in the field using satellite positioning technologies (Global Navigation Satellite Systems, GNSS) and the increasing availability of geographic information in digital form and in increasing quality, farmers are now able to measure the spatial and temporal variability in soil, vegetation, relief, etc. within a field and to modify their operations to react to this. Farmers keep electronic field records and farm diaries, which they are able to use on-site with a PDA in order to enter or retrieve information. Agricultural machinery is also being continuously developed, since due to the in-field heterogeneity many different operations (from yield mapping to plant protection) are performed on-site and logged so that they may be later evaluated by computer. Due to legal regulations (IACS, cross-compliance, traceability, quality management, etc.), GIS (and GeoWeb services) and information-driven crop production are becoming normal tools in agriculture, which must be integrated into usual farm practices.