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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 theupcoming decades caused by a growing world population, climate change, the needfor sustainable production, lack of skilled workers, and generally the limitedavailability of arable land. Vision systems could help cope with thesechallenges by offering tools to make better and more sustainable fieldmanagement decisions and support the breeding of new varieties of crops byallowing temporally dense and reproducible measurements. Recently, tacklingperception tasks in the agricultural domain got increasing interest in thecomputer vision and robotics community since agricultural robotics are onepromising solution for coping with the lack of workers and enable a moresustainable agricultural production at the same time. While large datasets andbenchmarks in other domains are readily available and have enabled significantprogress toward more reliable vision systems, agricultural datasets andbenchmarks are comparably rare. In this paper, we present a large dataset andbenchmarks for the semantic interpretation of images of real agriculturalfields. Our dataset recorded with a UAV provides high-quality, denseannotations of crops and weeds, but also fine-grained labels of crop leaves atthe same time, which enable the development of novel algorithms for visualperception in the agricultural domain. Together with the labeled data, weprovide novel benchmarks for evaluating different visual perception tasks on ahidden test set comprised of different fields: known fields covered by thetraining data and a completely unseen field. The tasks cover semanticsegmentation, panoptic segmentation of plants, leaf instance segmentation,detection of plants and leaves, and hierarchical panoptic segmentation forjointly identifying plants and leaves.

PhenoBench -- A Large Dataset and Benchmarks for Semantic Image Interpretation in the Agricultural Domain