用RGB-D图像自动估测作物茎秆直径，提升育种效率
Axis-Aligned 3D Stalk Diameter Estimation from RGB-D Imagery
- 结合深度学习与3D重建，通过PCA轴对齐切片减少弯曲影响
- 在真实农田场景中实现高精度茎秆直径估计，支持大规模表型分析
- 适合农业育种、精准农学研究者使用，无需复杂设备
精确、高效的表型分析是现代作物育种的关键，尤其在提升机械稳定性、生物量和抗病性方面。茎秆直径是重要结构性状，但传统测量方法耗时费力且易出错，难以规模化应用。本文提出一种基于RGB-D影像的几何感知视觉流程，通过深度学习实例分割、3D点云重建及主成分分析（PCA）实现轴对齐切片，有效缓解弯曲、遮挡和图像噪声的影响，提供了一种可扩展、可靠的高通量表型解决方案，适用于育种与农学研究。
原文摘要 · Abstract (English)
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.
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