发表机构
University of Bonn; CSIRO（波恩大学; 联邦科学与工业研究组织）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
论文发布了BUTom21和BUTom-ST21两个番茄数据集，前者含静止图像与手动标注，后者含视频数据与伪标签，均有果实成熟度像素级标签，为研究界提供真实图像集，助力番茄植株及果实状态感知研究，时空数据集拓展田间表型分析边界。
AI 中文摘要
在本论文中，我们发布了两个用于视觉感知商业化种植环境中番茄植株的数据集，这些数据由机器人采集。第一个是BUTom21，包含静止图像和手动标注。第二个是BUTom-ST21，包含基于视频的数据和通过基于人工智能方法的半自动标注（伪标签）。两种情况下都提供了果实成熟度的像素级标签。目的是为研究界提供具有挑战性的真实世界图像集，以探索感知和估计番茄植株及其果实状态的方法，这是一种重要的园艺作物。重要的是，时空数据集提供了单个果实数量和成熟度信息，使研究人员能够拓展基于田间表型分析的边界。
英文摘要
In this manuscript we release two datasets for visual sensing of tomato plants grown in commercial-like settings and acquired using a robot. The first is BUTom21 which consists of still images and manual annotations. The second is BUTom-ST21 which consists of video-based data and semi-automated annotations through AI-based methods, referred to as pseudo-labels. In both cases, we provide pixel-level labels for the ripeness of the fruit. The aim is to provide the research community a challenging set of real-world imagery to explore methods to sense and estimate the state of tomato plants and their fruit, which is an important horticultural crop. Importantly, the spatial-temporal dataset provides individual fruit count and ripeness information enabling researchers to push the boundaries of field-based phenotyping.
Comments21 pages, 2 figures, 9 tables. Two novel datasets released - link to repository in document
