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Auflistung nach Schlagwort "biodiversity"

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  • Textdokument
    A pragmatic approach to concept-based annotation of scientific names in biodiversity and environmental research data
    (INFORMATIK 2021, 2021) Kohlbecker,Andreas; Güntsch, Anton; Kilian, Norbert; Kusber, Wolf-Henning; Luther, Katja; Müller, Andreas; von Raab-Straube, Eckhard; Berendsohn, Walter
    With the increasing amount of interdisciplinary and international networks dedicated to long-term persistence and interoperability of research data, the demand for semantic linking of environmental research data has grown. Data related to organisms frequently inherit a major obstacle. Organisms often are ambiguously identified by using only the scientific name, which is not a precise identifier for the taxonomic concept that is implicitly being used. Here we describe a robust taxon concept definition that allows deducing a set of rules for semi-automatically managing concepts. These rules define specific taxonomic operations as transition points at which new taxon concepts emerge from former concepts. Implemented into the business logic of taxon management systems, these rules can assure the stability of taxon concepts so that environmental data sets can be reliably annotated with the corresponding persistent identifier. Our approach limits the risk that referenced taxon concepts are modified unnoticed.
  • Konferenzbeitrag
    Towards model-based automation of plant-specific weed regulation
    (43. GIL-Jahrestagung, Resiliente Agri-Food-Systeme, 2023) Renz, Marian; Niemeyer, Mark; Hertzberg, Joachim
    Weeds are commonly known as a major factor for yield losses in agriculture, competing with crops for resources like nutrients, water, and light. However, keeping specific weeds could benefit agricultural sites for example by nitrogen fixation, erosion protection, or increasing biodiversity. This comes with technological challenges like plant detection and classification, damage estimation, and selective removal. This paper presents a model-based approach to the problem of damage estimation of perceived plants. The system uses contextual and background knowledge in the form of rules about the plant count per square meter and the distance to the nearest crop together with thresholds for each weed species. The functionality is demonstrated using an artificial dataset and exemplary thresholds, showing the potential of using knowledge about plant-crop interactions for more sophisticated weed control systems.