van Marrewijk Bart M, van Daalen Tim, Smoleňová Katarína, Xin Bolai, Polder Gerrit, Kootstra Gert
Wageningen University and Research, Wageningen, the Netherlands.
College of Engineering, South China Agricultural University, Guangzhou, China.
Data Brief. 2025 Jul 7;61:111852. doi: 10.1016/j.dib.2025.111852. eCollection 2025 Aug.
Plant phenotyping involves the measurements of plant traits to gain more insight into the interaction between the genotype (G), environment (E) and crop management strategies (M). To improve plant phenotyping, accurate measurements are crucial. Manual measurements are biased, time-intensive, and therefore limited to only a few plants. Especially measurements of 3D phenotypic traits, such as plant architecture, internode length, and leaf area are difficult to extract manually. To enhance the speed and accuracy of phenotyping, there is a need for automatic digital plant phenotyping solutions. The presented dataset contains 3D point clouds of tomato plants, which will enable researchers to develop novel methods to extract 3D phenotypic traits. Converting 3D point clouds to plant traits is also known as 3D plant phenotyping. This process can be subdivided into three steps: point cloud segmentation, skeletonisation to extract plant architecture, and plant-traits extraction. Those three steps need to be analysed properly to indicate bottlenecks and improve 3D phenotyping algorithms. Currently, the development of 3D phenotyping algorithms is inhibited by the availability of comprehensive datasets and algorithms to analyse all steps. To our best knowledge only five annotated datasets exist for testing and validating 3D phenotyping algorithms. However, these datasets mainly focus on the segmentation step. Skeletonisation and manual measured plant traits are frequently not included. To improve 3D plant phenotyping, a novel dataset, TomatoWUR, is presented. This comprehensive dataset consists of 44 point clouds of single tomato plants imaged by fifteen cameras to create a point cloud using the shape-from-silhouette methodology. The dataset includes annotated point clouds, skeletons, and manual reference measurements. In addition, the dataset includes software for comprehensive evaluation and comparison of phenotyping methods, which is expected to benefit the development of 3D phenotyping algorithms. The related software can be found our GIT: https://github.com/WUR-ABE/TomatoWUR.
植物表型分析涉及对植物性状的测量，以便更深入地了解基因型（G）、环境（E）和作物管理策略（M）之间的相互作用。为了改进植物表型分析，精确测量至关重要。手动测量存在偏差且耗时，因此仅限于少数植物。特别是三维表型性状的测量，如植物结构、节间长度和叶面积，很难手动提取。为了提高表型分析的速度和准确性，需要自动数字植物表型分析解决方案。所提供的数据集包含番茄植株的三维点云，这将使研究人员能够开发提取三维表型性状的新方法。将三维点云转换为植物性状也被称为三维植物表型分析。这个过程可以细分为三个步骤：点云分割、提取植物结构的骨架化以及植物性状提取。需要对这三个步骤进行适当分析，以指出瓶颈并改进三维表型分析算法。目前，三维表型分析算法的发展受到综合数据集和分析所有步骤的算法可用性的限制。据我们所知，仅有五个带注释的数据集可用于测试和验证三维表型分析算法。然而，这些数据集主要关注分割步骤。骨架化和手动测量的植物性状通常不包括在内。为了改进三维植物表型分析，提出了一个新的数据集TomatoWUR。这个综合数据集由15台相机拍摄的44个单株番茄植株的点云组成，使用从轮廓形状方法创建点云。该数据集包括带注释的点云、骨架和手动参考测量。此外，该数据集还包括用于表型分析方法综合评估和比较的软件，预计这将有利于三维表型分析算法的发展。相关软件可在我们的GIT上找到：https://github.com/WUR-ABE/TomatoWUR。
