Axis-Aligned 3D Stalk Diameter Estimation from RGB-D Imagery
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 6 of its 16 references that are in the map. First posted on arXiv 15 Sep 2025.
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6 of 16 references.
References · 6
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- 2024DiffusionEdge: Diffusion Probabilistic Model for Crisp Edge Detectioncited by 33
- 2025RF-DETR Object Detection vs YOLOv12 : A Study of Transformer-based and CNN-based Architectures for Single-Class and Multi-Class Greenfruit Detection in Complex Orchard Environments Under Label Ambiguitycited by 12
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- 2024RowDetr: End-to-End Crop Row Detection Using Polynomialscited by 1
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