BonnBeetClouds3D: A Dataset Towards Point Cloud-based Organ-level
  Phenotyping of Sugar Beet Plants under Field Conditions
Elias Marks Jonas Bömer Federico Magistri Anurag Sah Jens Behley Cyrill Stachniss
Agricultural production is facing severe challenges in the next decades
induced by climate change and the need for sustainability, reducing its impact
on the environment. Advancements in field management through non-chemical
weeding by robots in combination with monitoring of crops by autonomous
unmanned aerial vehicles (UAVs) and breeding of novel and more resilient crop
varieties are helpful to address these challenges. The analysis of plant
traits, called phenotyping, is an essential activity in plant breeding, it
however involves a great amount of manual labor. With this paper, we address
the problem of automatic fine-grained organ-level geometric analysis needed for
precision phenotyping. As the availability of real-world data in this domain is
relatively scarce, we propose a novel dataset that was acquired using UAVs
capturing high-resolution images of a real breeding trial containing 48 plant
varieties and therefore covering great morphological and appearance diversity.
This enables the development of approaches for autonomous phenotyping that
generalize well to different varieties. Based on overlapping high-resolution
images from multiple viewing angles, we compute photogrammetric dense point
clouds and provide detailed and accurate point-wise labels for plants, leaves,
and salient points as the tip and the base. Additionally, we include
measurements of phenotypic traits performed by experts from the German Federal
Plant Variety Office on the real plants, allowing the evaluation of new
approaches not only on segmentation and keypoint detection but also directly on
the downstream tasks. The provided labeled point clouds enable fine-grained
plant analysis and support further progress in the development of automatic
phenotyping approaches, but also enable further research in surface
reconstruction, point cloud completion, and semantic interpretation of point
clouds.