Technical University of Munich, TUM Campus Straubing for Biotechnology and Sustainability, Bioinformatics, Schulgasse 22, 94315, Straubing, Germany.
Weihenstephan-Triesdorf University of Applied Sciences, Bioinformatics, Petersgasse 18, 94315, Straubing, Germany.
Sci Data. 2024 Jan 23;11(1):109. doi: 10.1038/s41597-024-02945-6.
Sustainable weed management strategies are critical to feeding the world's population while preserving ecosystems and biodiversity. Therefore, site-specific weed control strategies based on automation are needed to reduce the additional time and effort required for weeding. Machine vision-based methods appear to be a promising approach for weed detection, but require high quality data on the species in a specific agricultural area. Here we present a dataset, the Moving Fields Weed Dataset (MFWD), which captures the growth of 28 weed species commonly found in sorghum and maize fields in Germany. A total of 94,321 images were acquired in a fully automated, high-throughput phenotyping facility to track over 5,000 individual plants at high spatial and temporal resolution. A rich set of manually curated ground truth information is also provided, which can be used not only for plant species classification, object detection and instance segmentation tasks, but also for multiple object tracking.
可持续的杂草管理策略对于养活世界人口、保护生态系统和生物多样性至关重要。因此，需要基于自动化的特定地点杂草控制策略来减少除草所需的额外时间和精力。基于机器视觉的杂草检测方法似乎是一种很有前途的方法，但需要特定农业区域中物种的高质量数据。在这里，我们提出了一个数据集，即移动领域杂草数据集（MFWD），它捕获了在德国高粱和玉米田中常见的 28 种杂草的生长情况。总共在一个全自动、高通量的表型设施中采集了 94321 张图像，以高时空分辨率跟踪超过 5000 株单个植物。还提供了一组丰富的手动整理的地面实况信息，不仅可用于植物物种分类、目标检测和实例分割任务，还可用于多目标跟踪。
