Visualizations of Uncertainties in Precision Agriculture: Lessons Learned from Farm Machinery
Source abstract
Abstract
Detailed measurements of yield values are becoming a common practice in precision agriculture. Field harvesters generate point Big Data as they provide yield measurements together with dozens of complex attributes in a frequency of up to one second. Such a flood of data brings uncertainties caused by several factors: accuracy of the positioning system used, trajectory overlaps, raising the cutting bar due to obstacles or unevenness, and so on. This paper deals with 2D and 3D cartographic visualizations of terrain, measured yield, and its uncertainties. Four graphic variables were identified as credible for visualizations of uncertainties in point Big Data. Data from two plots at a fully operational farm were used for this purpose. ISO 19157 was examined for its applicability and a proof-of-concept for selected uncertainty expression was defined. Special attention was paid to spatial pattern interpretations.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Soil Geostatistics and Mapping · Remote Sensing and LiDAR Applications · Smart Agriculture and AI
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Geography, Faculty of Science, Masaryk University, Kotlářská 2, 611 37 Brno, Czech Republic
Plan4All, K Rybníčku 557, 330 12 Horní Bříza, Czech Republic
Funding
Horizon 2020
References (97)
Show source-provided references
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