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

arXiv: https://arxiv.org/abs/2509.12511 · HTML: https://mlsanity.com/paper/2509.12511/

## Key facts

- Cited by 1 papers 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.

## Builds on (most cited first, 6 of 6)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Open3D: A Modern Library for 3D Data Processing](https://mlsanity.com/paper/1801.09847/) (2018)
- [DiffusionEdge: Diffusion Probabilistic Model for Crisp Edge Detection](https://mlsanity.com/paper/2401.02032/) (2024)
- [RF-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 Ambiguity](https://mlsanity.com/paper/2504.13099/) (2025)
- [PST: Plant segmentation transformer for 3D point clouds of rapeseed plants at the podding stage](https://mlsanity.com/paper/2206.13082/) (2022)
- [RowDetr: End-to-End Crop Row Detection Using Polynomials](https://mlsanity.com/paper/2412.10525/) (2024)

## Built on by (most cited first, 1 of 1)

- [FruitProm: Probabilistic Maturity Estimation and Detection of Fruits and Vegetables](https://mlsanity.com/paper/2510.24885/) (2025)

## 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.