The Unified Modeling Language user guide
The Unified Modeling Language user guide
Computers and Electronics in Agriculture
Conceptual model of a future farm management information system
Computers and Electronics in Agriculture
A user-centric approach for information modelling in arable farming
Computers and Electronics in Agriculture
Original papers: Functional requirements for a future farm management information system
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture
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The understanding of actual and potential data-flows in the practice of precision agriculture (PA) is an essential prerequisite for the optimisation and automation of information management in this field. As a contribution to this process, this paper presents an analysis of the data-flows within the work of a collaborative research project concerned with the testing of the methods developed within the project on two demonstration fields. This work provides a good case study for the modelling of a range of data-flows covering a broad spectrum of PA techniques. Using the notation and software tools for the Unified Modeling Language (UML), a complete model of all identified data-flows was created. Individual data-streams relating to particular source or product datasets were then extracted from this model. These data-streams present a practical application of the model in identifying the benefit that may be obtained from a particular gathered dataset (e.g. yield data) or in identifying the data that must be gathered to generate a particular product dataset (e.g. sustainability indicators). Whilst the current model is focussed on one particular research project, it has potential to be extended to cover more generally the common practice of precision agriculture. Such a model may then be used by farmers as a roadmap for the adoption for precision agriculture by allowing them to determine what datasets are available to them or may be easily collected and what products they may generate from these, or vice versa to identify what datasets they must obtain in order to generate a particular dataset of interest.