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Auflistung nach Schlagwort "precision farming"

1 - 6 von 6
  • Konferenzbeitrag
    CherryGraph: Encoding digital twins of cherry trees into a knowledge graph based on topology
    (44. GIL - Jahrestagung, Biodiversität fördern durch digitale Landwirtschaft, 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.
  • Konferenzbeitrag
    A data quality assessment tool for agricultural structured data as support for smart farming
    (43. GIL-Jahrestagung, Resiliente Agri-Food-Systeme, 2023) Schroth, Christof; Kelbert, Patricia; Vollmer, Anna Maria
    In the field of precision farming or smart farming, more and more sensors are used and produce a massive amount of data. Examples are machinery, weather stations, or georeferenced data, which can be used, among other things, by Artificial Intelligence decision support systems to improve or facilitate farmers’ daily work tasks. Even if there are no issues in transferring (Internet of Things) sensor data from machines to farm management information systems, data still contain errors such as missing, implausible, or incorrect data values. In this paper, we present an automated data quality assessment (DQA) tool based on the ISO25012 standard. We describe the process of how we developed this tool with support from practitioners who produce agricultural data in the context of the EU Horizon 2020 project DEMETER. Additionally, we highlight some of the requirements we collected for such a tool and briefly discuss how we addressed them. For example, we learned that in the context of developing smart farming services, the data quality dimensions Accuracy, Completeness, Consistency, and Credibility are the most important ones for practitioners such as farmers, digital service providers, or machine suppliers. Therefore, we included them in the DQA tool and implemented it in Python. It is released under the open-source Apache 2 license. Individual parameters can be provided as input for calculations (e.g., thresholds or time lengths) to meet different users’ needs. The output of the DQA is provided in machine-readable JSON format and can be used for further analysis, e.g., to improve the quality of the data collection or the follow-up data analysis. This can help practitioners develop more valuable smart farming services.
  • Konferenzbeitrag
    Development of an index to estimate potential risk of slug damage
    (44. GIL - Jahrestagung, Biodiversität fördern durch digitale Landwirtschaft, 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.
  • Konferenzbeitrag
    For5G: Systematic approach for creating digital twins of cherry orchards
    (43. GIL-Jahrestagung, Resiliente Agri-Food-Systeme, 2023) Meyer, Lukas; Gilson, Andreas; Uhrmann, Franz; Weule, Mareike; Keil, Fabian; Haunschild, Bernhard; Oschek, Joachim; Steglich, Marco; Hansen, Jonathan; Stamminger, Marc; Scholz, Oliver
    We present a systematic approach for creating digital twins of cherry trees in orchards as part of the project “For5G: Digital Twin”. We aim to develop a basic concept for 5G applications in orchards using a mobile campus network. Digital twins monitor the status of individual trees in every aspect and are a crucial step for the digitalization of processes in horticulture. Our framework incorporates a transformation of photometric data to a 3D reconstruction, which is subsequently segmented and modeled using learning-based approaches. Collecting objective phenotypic features from individual trees over time and storing them in a knowledge graph offers a convenient foundation for gaining new insights. Our approach shows promising results at this point for creating a detailed digital twin of a cherry tree and ultimately the entire orchard.
  • Konferenzbeitrag
    Model for the calculation of soil compaction on agricultural land
    (44. GIL - Jahrestagung, Biodiversität fördern durch digitale Landwirtschaft, 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.
  • Konferenzbeitrag
    Route-planning in output-material-flow arable farming operations aiming for soil protection
    (42. GIL-Jahrestagung, Künstliche Intelligenz in der Agrar- und Ernährungswirtschaft, 2022) Focke Martinez, Santiago; Hertzberg, Joachim
    This paper presents two approaches for route planning in output-material-flow arable farming: one for time optimization and one for soil protection. The two approaches were used to plan the routes of one harvester and one transport vehicle performing a harvesting operation in a test field, and were compared by analyzing the operation duration, travel distance, and area driven over by the machines. The results show the benefits and drawbacks of planning the machine routes using the proposed method for soil protection: the plans can reduce the impact of driving over the soil, but it can result in higher operation durations and traveled distances