TomatoWUR: an annotated dataset of 3D tomato plants to quantitatively evaluate segmentation, skeletonisation, and plant trait extraction algorithms for 3D plant phenotyping

In this research, we created a dataset of 44 annotated 3D point clouds of tomato plants. Images were captured using fifteen cameras surrounding a single plant. Those images were used to create a point cloud using the shape-from silhouette methodology (Golbach et al., 2016). The resulting dataset was annotated and separated into three parts. 1) Annotated point clouds and corresponding RGB images with semantic and instance labels , 2) annotated skeletons to analyse plant architecture, and 3) manual reference measurements of internode length, internode diameter, leaf angle, and phyllotactic angle to evaluate phenotyping algorithms from point cloud to plant traits.

本研究构建了一套包含44份带标注番茄植株三维点云（3D point cloud）的数据集。该数据集的采集与构建流程如下：使用环绕单株植株的15台相机采集图像，随后通过轮廓形状重建法（shape-from silhouette，Golbach等，2016）基于所获图像生成点云。所得数据集经标注后被划分为三个部分：1. 搭载语义与实例标注的点云及对应RGB图像；2. 用于植株株型分析的标注骨架；3. 用于评估点云到植株性状表型算法的人工参考测量数据，具体包括节间长度、节间直径、叶夹角及叶序角。

搜集汇总

背景与挑战

背景概述

TomatoWUR是一个用于3D植物表型分析的番茄植物数据集，包含44个带注释的3D点云，通过15个摄像头拍摄图像并使用形状从轮廓方法生成。数据集特点在于提供多模态注释（包括语义和实例标签的RGB图像、注释骨架）以及手动参考测量值（如节间长度和叶角），旨在定量评估分割、骨架化和植物性状提取算法，适用于人工智能和农业科学研究。