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001047611 005__ 20251217202225.0
001047611 0247_ $$2datacite_doi$$a10.34734/FZJ-2025-04413
001047611 037__ $$aFZJ-2025-04413
001047611 041__ $$aEnglish
001047611 1001_ $$0P:(DE-Juel1)191034$$aYildiz, Erenus$$b0$$ufzj
001047611 245__ $$a3D Reconstruction of Cassava Roots Using COLMAP
001047611 260__ $$c2025
001047611 300__ $$a46p
001047611 3367_ $$2DRIVER$$areport
001047611 3367_ $$2ORCID$$aREPORT
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001047611 520__ $$aCassava (Manihot esculenta) is an important crop for food security in tropical and subtropical regions, with roots containing up to 85% starch on a dry weight basis [25]. Understanding root system architecture is essential for breeding programs aimed at improving yield and stress tolerance [1]. Traditional 2D imaging methods for root phenotyping have limitations in capturing threedimensional root structures, leading to incomplete trait measurements [28, 8]. While advanced 3D methods like DIRT/3D 2.0 exist [14], they require specialized equipment that may not be accessible to all research facilities. This laboratory work implemented a cost-effective 3D reconstruction pipeline using standard DSLR cameras and open-source software (REMBG, COLMAP) to extract morphological traits from cassava roots. The pipeline combined deep learning-based segmentation with structure from motion techniques to analyze 1039 cassava root system acquisitions from plants aged 5-10 weeks collected at the Rayong Field Crops Research Center in Thailand, achieving 62% reconstruction success (644 successfully reconstructed roots).
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001047611 65027 $$0V:(DE-MLZ)SciArea-220$$2V:(DE-HGF)$$aInstrument and Method Development$$x0
001047611 7001_ $$0P:(DE-Juel1)129394$$aScharr, Hanno$$b1$$eContributor$$ufzj
001047611 7001_ $$0P:(DE-Juel1)156560$$aWojciechowski, Tobias$$b2$$eContributor$$ufzj
001047611 7001_ $$0P:(DE-Juel1)165832$$aBoeckem, Vera$$b3$$eContributor$$ufzj
001047611 8564_ $$uhttps://juser.fz-juelich.de/record/1047611/files/3D%20Reconstruction%20of%20Cassava%20Roots%20-%20LabReport.pdf$$yOpenAccess
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001047611 9141_ $$y2025
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