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Automated labeling of soybeans for size measurements

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dc.contributor.authorBicici, Ufuk Can
dc.contributor.authorTrimmel, Matthias
dc.contributor.authorRiegler-Nurscher, Peter
dc.contributor.editorDörr, Jörg
dc.contributor.editorSteckel, Thilo
dc.date.accessioned2025-02-04T14:37:59Z
dc.date.available2025-02-04T14:37:59Z
dc.date.issued2025
dc.description.abstractSoybeans are crucial in agriculture and industry, serving as a key source of protein and oil for food, feed, and various applications. Measuring individual soybean seed properties, such as size, color, and texture, aids in predicting batch parameters. In a conveyor belt setup, images captured by an RGB camera are analyzed using computer vision techniques, such as object detection and segmentation, to identify whole beans, which are then used for size measurements. For such a supervised machine learning approach, annotated data is essential. To address the challenge of manual data labeling, we propose an automated annotation system for identifying beans on a conveyor belt. Using the Segment Anything Model (SAM), contours are extracted, and ellipses are fitted to approximate bean shapes. In a dataset of 17,386 images, SAM generated over 3.5 million contours, with approximately 265,000 annotated as individual beans. Preliminary results from a deep learning-based ellipse detection model and a panoptic segmentation model, both trained on the generated soybean dataset, are presented.en
dc.identifier.doi10.18420/giljt2025_17
dc.identifier.eissn2944-7682
dc.identifier.isbn978-3-88579-802-6
dc.identifier.pissn2944-7682
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/45674
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.subjectsoybean
dc.subjectsoybean size detection
dc.subjectmachine learning
dc.subjectcomputer vision
dc.subjectdeep learning
dc.titleAutomated labeling of soybeans for size measurementsen
dc.typeText/Conference Paper
gi.citation.endPage212
gi.citation.publisherPlaceBonn
gi.citation.startPage207
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