P344 - 44. GIL-Jahrestagung 2024 - Fokus: Biodiversität fördern durch digitale Landwirtschaft

Permanent URI for this collectionhttps://dl.gi.de/handle/20.500.12116/43863

Authors with most Documents  

Ackermann, Andrea
2
Assmann, Denise
2
Aue, Alexander
2
Becker, Rolf
2
Conrad, Christopher
2

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Subject: search.filters.subject.precision farming

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  • Conference Paper
    Model for the calculation of soil compaction on agricultural land
    (Gesellschaft für Informatik e.V., 2024) Westerkamp, Clemens; Thünemann, Christian; Schaarschmidt, Marco
    The risk of soil compaction is a growing concern in agriculture as machinery becomes larger and larger. In this paper, a model is presented that generates a spatial estimation of the soil compaction based on soil survey mapping, soil moisture data and machinery data. The Soil Compaction Index describes the risk of harmful compaction of soil. Feasibility and deployment as an Agri-Gaia service were evaluated by an application for researchers and practitioners to predict areas with high soil compaction risk and adapt agricultural processes accordingly.
  • Conference Paper
    Development of an index to estimate potential risk of slug damage
    (Gesellschaft für Informatik e.V., 2024) Giovanni Antonio Puliga, Jobst Gödeke
    Terrestrial slugs are important pests for many agricultural and horticultural crops. Current control strategies are mostly based on preventive approaches and their success is strongly influenced by timing of application and knowledge of the pests’ behaviour. This paper presents an approach to estimate spatial and temporal activity of slugs in the field. For this, an index is developed considering different factors that influence the activity of slugs. The index is then used to generate a map, where areas of the field with higher potential risk of slug damage are identified. This map can be used for smart agriculture applications such as the control of these pests through an autonomously operating field robot.
  • Conference Paper
    CherryGraph: Encoding digital twins of cherry trees into a knowledge graph based on topology
    (Gesellschaft für Informatik e.V., 2024) Andreas Gilson, Mareike Weule
    CherryGraph is a structural framework for mapping trees into an ontology-based knowledge graph that can be used as database backend for digital twins. Based on the reconstructed 3D topology of scanned trees, information is encoded in a knowledge graph that resembles the real canopy structure of trees. Thus, CherryGraph enables consistent navigation within the branching system of a tree over different time points regardless of natural fluctuations. The resulting knowledge graph can then be queried for arbitrary use cases or aggregated on different hierarchy levels. We demonstrate the potential of CherryGraph by using data of real cherry trees from the 2023 cherry season with exemplary queries that can be extended to include spatial and temporal dimensions for comparing indicators like elongation growth of shoots or tracking the development of other various tree traits over time.
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