Abstract
BackgroundThe 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.ResultsWe 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.ConclusionWe 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.
Highlights
The production and availability of annotated data sets are indispensable for training and evaluation of automatic phenotyping methods
Automatic plant phenotyping through 3D data has been a recent research topic in computer vision; the scarcity of labeled and complete models of real plants is a roadblock for applying recent machine learning techniques that rely on a vast amount of annotated data
We provide the ROSE-X data set of 11 complete 3D models of real potted rosebush plants with complex architecture acquired through X-ray computed tomography
Summary
The production and availability of annotated data sets are indispensable for training and evaluation of automatic phenotyping methods. Automatic plant phenotyping through 3D data has been a recent research topic in computer vision; the scarcity of labeled and complete models of real plants is a roadblock for applying recent machine learning techniques that rely on a vast amount of annotated data. The success of deep learning methods has triggered the interest in data collection and labeling in specific applications of computer vision such as plant imaging [3]. Most of the freely available annotated plant shoot data sets so far have been in the form of collections of 2D images acquired in the visible spectrum from top or side view.
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