Dutagaci Helin, Rasti Pejman, Galopin Gilles, Rousseau David
1LARIS, UMR INRA IRHS, Université d'Angers, 62 Avenue Notre Dame du Lac, 49000 Angers, France.
2INRA, UMR1345 Institut de Recherche en Horticulture et Semences, 42 Georges Morel CS 60057, 49071 Beaucouze, France.
Plant Methods. 2020 Mar 4;16:28. doi: 10.1186/s13007-020-00573-w. eCollection 2020.
The production and availability of annotated data sets are indispensable for training and evaluation of automatic phenotyping methods. The need for complete 3D models of real plants with organ-level labeling is even more pronounced due to the advances in 3D vision-based phenotyping techniques and the difficulty of full annotation of the intricate 3D plant structure.
We introduce the ROSE-X data set of 11 annotated 3D models of real rosebush plants acquired through X-ray tomography and presented both in volumetric form and as point clouds. The annotation is performed manually to provide ground truth data in the form of organ labels for the voxels corresponding to the plant shoot. This data set is constructed to serve both as training data for supervised learning methods performing organ-level segmentation and as a benchmark to evaluate their performance. The rosebush models in the data set are of high quality and complex architecture with organs frequently touching each other posing a challenge for the current plant organ segmentation methods. We report leaf/stem segmentation results obtained using four baseline methods. The best performance is achieved by the volumetric approach where local features are trained with a random forest classifier, giving Intersection of Union (IoU) values of 97.93% and 86.23% for leaf and stem classes, respectively.
We provided an annotated 3D data set of 11 rosebush plants for training and evaluation of organ segmentation methods. We also reported leaf/stem segmentation results of baseline methods, which are open to improvement. The data set, together with the baseline results, has the potential of becoming a significant resource for future studies on automatic plant phenotyping.
带注释数据集的生成与可用性对于自动表型分析方法的训练和评估不可或缺。由于基于3D视觉的表型分析技术的进步以及对复杂3D植物结构进行完整注释的困难，对带有器官级标注的真实植物完整3D模型的需求更加突出。
我们引入了ROSE-X数据集，该数据集包含通过X射线断层扫描获取的11个带注释的真实玫瑰丛植物3D模型，以体素形式和点云形式呈现。注释是手动进行的，以便为与植物枝条对应的体素提供器官标签形式的地面真值数据。该数据集既作为执行器官级分割的监督学习方法的训练数据，又作为评估其性能的基准。数据集中的玫瑰丛模型质量高且结构复杂，器官之间经常相互接触对当前的植物器官分割方法构成挑战。我们报告了使用四种基线方法获得的叶/茎分割结果。体积法表现最佳，其中使用随机森林分类器训练局部特征，叶类和茎类的交并比（IoU）值分别为97.93%和86.23%。
我们提供了一个包含11个玫瑰丛植物的带注释3D数据集用于器官分割方法的训练和评估。我们还报告了基线方法的叶/茎分割结果，这些结果仍有待改进。该数据集以及基线结果有可能成为未来自动植物表型分析研究的重要资源。
