  The production of food, feed, fiber, and fuel is a key task of agriculture,
which has to cope with many challenges in the upcoming decades, e.g., a higher
demand, climate change, lack of workers, and the availability of arable land.
Vision systems can support making better and more sustainable field management
decisions, but also support the breeding of new crop varieties by allowing
temporally dense and reproducible measurements. Recently, agricultural robotics
got an increasing interest in the vision and robotics communities since it is a
promising avenue for coping with the aforementioned lack of workers and
enabling more sustainable production. While large datasets and benchmarks in
other domains are readily available and enable significant progress,
agricultural datasets and benchmarks are comparably rare. We present an
annotated dataset and benchmarks for the semantic interpretation of real
agricultural fields. Our dataset recorded with a UAV provides high-quality,
pixel-wise annotations of crops and weeds, but also crop leaf instances at the
same time. Furthermore, we provide benchmarks for various tasks on a hidden
test set comprised of different fields: known fields covered by the training
data and a completely unseen field. Our dataset, benchmarks, and code are
available at \url{https://www.phenobench.org}.
