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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2509.12511 |
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| _version_ | 1866912588790497280 |
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| author | Vail, Benjamin Cheppally, Rahul Harsha Sharda, Ajay Rai, Sidharth |
| author_facet | Vail, Benjamin Cheppally, Rahul Harsha Sharda, Ajay Rai, Sidharth |
| contents | Accurate, high-throughput phenotyping is a critical component of modern crop breeding programs, especially for improving traits such as mechanical stability, biomass production, and disease resistance. Stalk diameter is a key structural trait, but traditional measurement methods are labor-intensive, error-prone, and unsuitable for scalable phenotyping. In this paper, we present a geometry-aware computer vision pipeline for estimating stalk diameter from RGB-D imagery. Our method integrates deep learning-based instance segmentation, 3D point cloud reconstruction, and axis-aligned slicing via Principal Component Analysis (PCA) to perform robust diameter estimation. By mitigating the effects of curvature, occlusion, and image noise, this approach offers a scalable and reliable solution to support high-throughput phenotyping in breeding and agronomic research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_12511 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Axis-Aligned 3D Stalk Diameter Estimation from RGB-D Imagery Vail, Benjamin Cheppally, Rahul Harsha Sharda, Ajay Rai, Sidharth Computer Vision and Pattern Recognition Accurate, high-throughput phenotyping is a critical component of modern crop breeding programs, especially for improving traits such as mechanical stability, biomass production, and disease resistance. Stalk diameter is a key structural trait, but traditional measurement methods are labor-intensive, error-prone, and unsuitable for scalable phenotyping. In this paper, we present a geometry-aware computer vision pipeline for estimating stalk diameter from RGB-D imagery. Our method integrates deep learning-based instance segmentation, 3D point cloud reconstruction, and axis-aligned slicing via Principal Component Analysis (PCA) to perform robust diameter estimation. By mitigating the effects of curvature, occlusion, and image noise, this approach offers a scalable and reliable solution to support high-throughput phenotyping in breeding and agronomic research. |
| title | Axis-Aligned 3D Stalk Diameter Estimation from RGB-D Imagery |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.12511 |