Nguyen, Xuan KhanhBraun, BastianHeider, NicoSchieck, MartinDörr, JörgSteckel, Thilo2025-02-042025-02-042025978-3-88579-802-6https://dl.gi.de/handle/20.500.12116/45717Deep 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.encomputer visionapple blossom phenologyexplainable AIprecision agricultureRecognition of phenological development stages of apple blossoms using computer visionText/Conference Paper10.18420/giljt2025_092944-76822944-7682