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.

翻译：粮食、饲料、纤维和燃料生产是农业的关键任务。尤其是作物生产在未来几十年必须应对由世界人口增长、气候变化、可持续生产需求、技能型劳动力短缺以及耕地面积有限等多重挑战。视觉系统可通过提供工具辅助制定更优且更可持续的田间管理决策，并通过支持高时间密度、可重复测量的方式促进作物新品种育种，从而助力应对这些挑战。近年来，由于农业机器人是解决劳动力短缺并同时实现更可持续农业生产的有前景方案之一，计算机视觉与机器人学界对农业领域感知任务的关注度日益提升。尽管其他领域已具备丰富的大型数据集与基准，并显著推动了更可靠视觉系统的进步，但农业领域的数据集与基准仍相对匮乏。本文提出一个用于真实农田图像语义解读的大型数据集与基准。该数据集通过无人机采集，不仅提供作物与杂草的高质量密集标注，还同时包含作物叶片细粒度标注，这有助于开发面向农业领域的视觉感知新算法。伴随标注数据，我们提供全新基准，用于在由不同田块构成的隐藏测试集上评估各类视觉感知任务：涵盖训练数据覆盖的已知田块与完全未知的田块。任务覆盖语义分割、植物全景分割、叶片实例分割、植株与叶片检测，以及联合识别植株与叶片的分层全景分割。