PhenoBench -- A Large Dataset and Benchmarks for Semantic Image
  Interpretation in the Agricultural Domain
Jan Weyler Federico Magistri Elias Marks Yue Linn Chong Matteo Sodano Gianmarco Roggiolani Nived Chebrolu Cyrill Stachniss Jens Behley
The production of food, feed, fiber, and fuel is a key task of agriculture.
Especially crop production has to cope with a multitude of challenges in the
upcoming decades caused by a growing world population, climate change, the need
for sustainable production, lack of skilled workers, and generally the limited
availability of arable land. Vision systems could help cope with these
challenges by offering tools to make better and more sustainable field
management decisions and support the breeding of new varieties of crops by
allowing temporally dense and reproducible measurements. Recently, tackling
perception tasks in the agricultural domain got increasing interest in the
computer vision and robotics community since agricultural robotics are one
promising solution for coping with the lack of workers and enable a more
sustainable agricultural production at the same time. While large datasets and
benchmarks in other domains are readily available and have enabled significant
progress toward more reliable vision systems, agricultural datasets and
benchmarks are comparably rare. In this paper, we present a large dataset and
benchmarks for the semantic interpretation of images of real agricultural
fields. Our dataset recorded with a UAV provides high-quality, dense
annotations of crops and weeds, but also fine-grained labels of crop leaves at
the same time, which enable the development of novel algorithms for visual
perception in the agricultural domain. Together with the labeled data, we
provide novel benchmarks for evaluating different visual perception tasks on a
hidden test set comprised of different fields: known fields covered by the
training data and a completely unseen field. The tasks cover semantic
segmentation, panoptic segmentation of plants, leaf instance segmentation,
detection of plants and leaves, and hierarchical panoptic segmentation for
jointly identifying plants and leaves.