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Auflistung nach Autor:in "Heider, Nico"

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  • Konferenzbeitrag
    Recognition of phenological development stages of apple blossoms using computer vision
    (45. GIL-Jahrestagung, Digitale Infrastrukturen für eine nachhaltige Land-, Forst- und Ernährungswirtschaft, 2025) Nguyen, Xuan Khanh; Braun, Bastian; Heider, Nico; Schieck, Martin
    Deep learning-based computer vision is increasingly supporting precision agriculture in orchards, reducing reliance on manual monitoring by trained specialists. This work presents an approach for automated monitoring of apple blossom growth stages, an important task for optimizing yield and quality in orchard management. We (1) construct an annotated dataset of hourly images capturing apple blossoms across BBCH stages 53 to 71, (2) develop convolutional neural networks (CNNs) for growth stage classification, and (3) validate model performance using explainable AI (XAI) to ensure interpretability. Our best-performing model achieves a classification accuracy of 93.1%, demonstrating strong potential for integration into Farm Management Information Systems for data-driven orchard management. Model interpretability analysis further reveals that, with adequate training data, the network predominantly relies on features within the blossom itself to inform predictions, suggesting robustness in real-world application scenarios.
  • Konferenzbeitrag
    A survey of datasets for computer vision in agriculture
    (45. GIL-Jahrestagung, Digitale Infrastrukturen für eine nachhaltige Land-, Forst- und Ernährungswirtschaft, 2025) Heider, Nico; Gunreben, Lorenz; Zürner, Sebastian; Schieck, Martin
    In agricultural research, there has been a recent surge in the amount of Computer Vision (CV) focused work. But unlike general CV research, large high-quality public datasets are sparsely available. This can be partially attributed to the high variability between different agricultural tasks, crops and environments as well as the complexity of data collection, but it is also influenced by the reticence to publish datasets by many authors. This, as well as the lack of a widely used agricultural data repository, are impactful factors that hinder research in applied CV for agriculture as well as the usage of agricultural data in general-purpose CV research. In this survey, we provide a large number of high-quality datasets of images taken on fields. Overall, we find 45 datasets, which are listed in this paper as well as in an online catalog on the project website: https://smartfarminglab.github.io/field_dataset_survey/.