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.本研究构建了包含44株带标注番茄植株三维点云（3D point cloud）的数据集。研究采用环绕单株番茄植株的15台相机采集图像，并结合基于轮廓的形状重建法（shape-from silhouette，Golbach等，2016）生成对应三维点云。所得数据集经标注并划分为三个部分：1. 带有语义标签与实例标签的标注三维点云及对应RGB图像（RGB）；2. 用于分析植株株型结构的标注骨架；3. 用于评估从三维点云到植株性状的表型分析算法的手动参考测量数据，测量指标涵盖节间长度、节间直径、叶夹角与叶序角。

搜集汇总

背景与挑战

背景概述

该数据集包含44个带注释的3D番茄植物点云，旨在定量评估用于3D植物表型的分割、骨架化和植物性状提取算法。数据集由多摄像头图像生成，包括注释点云、RGB图像、植物骨架和手动参考测量值，覆盖节间长度、叶角等关键性状，适用于农业人工智能和园艺研究。