BonnBeetClouds3D

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.
Advancement in field management through non-chemical weed-
ing 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 is called
phenotyping, and 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.
However, the availability of real-world data for such fine-
grained perception tasks in this domain is relatively scarce
compared to other domains such as autonomous driving. To
work towards closing this gap, we propose a novel dataset
that was acquired using UAVs capturing high-resolution im-
ages of a real breeding trial. This has the big advantage of
containing a multitude of plant varieties, leading to a great
morphological and appearance diversity covered by our dataset.
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 via
bundle adjustment that capture the geometric structure of
the plants. We provide detailed and accurate point-wise labels
for individual plants, individual leaves, salient points on the
leaves such as the tip and the base. Additionally we include
measurements of phenotypic traits performed by experts from
the German Federal Plant Variety Office (”Bundessortenamt)
on the real plants, allowing to evaluate 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 opens the door for
further progress in the development of automatic phenotyping
approaches, but also enable further research in closely related
application areas such as surface reconstruction, point cloud
completion, and semantic interpretation of point clouds.