Axis-Aligned 3D Stalk Diameter Estimation from RGB-D Imagery

Abstract

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.

Plant phenotyping relevance

RGB-D画像から作物の茎径を推定するコンピュータビジョン手法を開発しており、植物形質の取得・抽出が研究の中心であるため。

Code and data availability

The paper describes a custom RGB-D stalk diameter pipeline with a small artificial-plant dataset (93 images), a fine-tuned YOLOv11x-seg model, and CSV outputs, but contains no data availability statement, no public repository, and no author-provided URL for the dataset, images, code, or trained model. All cited URLs (U

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