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Auflistung nach Schlagwort "agricultural machinery"

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  • Konferenzbeitrag
    A data mining process for building recommendation systems for agricultural machines based on big data
    (42. GIL-Jahrestagung, Künstliche Intelligenz in der Agrar- und Ernährungswirtschaft, 2022) Altaleb, Mohamed; Deeken, Henning; Hertzberg, Joachim
    There is a potential expansion in the agricultural machinery industry by using the collected data from different years. Big data is already being used in other industries like e-commerce to improve decision-making processes. There are several existing process models to lead through the generic processes of data mining. The common factor between the process models that have attained dominant public position is that they are domain-agnostic frameworks. This paper proposes a method to extend the CRoss-Industry Standard Process for Data Mining (CRISP-DM) to focus on the agricultural domain and give guidelines on how to handle and structure the agricultural data and processes to reach defined data mining goals. The paper provides a walk-through for a use case to build a recommendation system.
  • Konferenzbeitrag
    Field plant characterization method based on a multi-wavelength line profiling system
    (41. GIL-Jahrestagung, Informations- und Kommunikationstechnologie in kritischen Zeiten, 2021) Pamornnak, Burawich; Scholz, Christian; Nieberg, Dominik; Igelbrink, Matthias; Ruckelshausen, Arno
    Phenotyping of plant characteristics is essential for plant breeding. Especially the growth stages of plants during field emergence, described by parameters such as plant height and plant counting, are of interest. But large-scale manual phenotyping is very inconvenient due to the workload, the harsh weather conditions, and time-consumption. Therefore, an automated system is needed. This research describes a field plant characterization method implemented in a plot divider machine for rapeseeds. The method consists of a plant height estimation and a plant counting system. Based on a multi-wavelength line profiling (MWLP) sensor system, the 2D and 3D point cloud information from visible wavelengths to near-infrared (NIR) are automatically mapped without any need for a matching method. The plant characterization processes consist of two main steps, 1) plant detection, and 2) height estimation. These processes use the 2D NIR and 3D point cloud as the main features. The proposed method was demonstrated with highly accurate results in several rapeseeds, illustrating the potential of this method to become a basic tool for crop characterization in plant sciences