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Recognition of phenological development stages of apple blossoms using computer vision

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dc.contributor.authorNguyen, Xuan Khanh
dc.contributor.authorBraun, Bastian
dc.contributor.authorHeider, Nico
dc.contributor.authorSchieck, Martin
dc.contributor.editorDörr, Jörg
dc.contributor.editorSteckel, Thilo
dc.date.accessioned2025-02-04T14:38:03Z
dc.date.available2025-02-04T14:38:03Z
dc.date.issued2025
dc.description.abstractDeep 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.en
dc.identifier.doi10.18420/giljt2025_09
dc.identifier.eissn2944-7682
dc.identifier.isbn978-3-88579-802-6
dc.identifier.pissn2944-7682
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/45717
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartof45. GIL-Jahrestagung, Digitale Infrastrukturen für eine nachhaltige Land-, Forst- und Ernährungswirtschaft
dc.relation.ispartofseriesLecture Notes in Informatics(LNI) - Proceedings, Volume P - 358
dc.subjectcomputer vision
dc.subjectapple blossom phenology
dc.subjectexplainable AI
dc.subjectprecision agriculture
dc.titleRecognition of phenological development stages of apple blossoms using computer visionen
dc.typeText/Conference Paper
gi.citation.endPage130
gi.citation.publisherPlaceBonn
gi.citation.startPage119
gi.conference.date25/26. Februar 2025
gi.conference.locationWieselburg, Austria
gi.conference.reviewfull

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P358 - 45. GIL-Jahrestagung 2025 - Fokus: Digitale Infrastrukturen für eine nachhaltige Land-, Forst- und Ernährungswirtschaft