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| Hauptseite > Publikationsdatenbank > 3D Reconstruction of Cassava Roots Using COLMAP > print |
| 001 | 1047611 | ||
| 005 | 20251217202225.0 | ||
| 024 | 7 | _ | |a 10.34734/FZJ-2025-04413 |2 datacite_doi |
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| 100 | 1 | _ | |a Yildiz, Erenus |0 P:(DE-Juel1)191034 |b 0 |u fzj |
| 245 | _ | _ | |a 3D Reconstruction of Cassava Roots Using COLMAP |
| 260 | _ | _ | |c 2025 |
| 300 | _ | _ | |a 46p |
| 336 | 7 | _ | |a report |2 DRIVER |
| 336 | 7 | _ | |a REPORT |2 ORCID |
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| 336 | 7 | _ | |a Internal Report |b intrep |m intrep |0 PUB:(DE-HGF)15 |s 1765971873_20224 |2 PUB:(DE-HGF) |
| 520 | _ | _ | |a Cassava (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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| 700 | 1 | _ | |a Scharr, Hanno |0 P:(DE-Juel1)129394 |b 1 |e Contributor |u fzj |
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| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1047611/files/3D%20Reconstruction%20of%20Cassava%20Roots%20-%20LabReport.pdf |y OpenAccess |
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