发布两个番茄植株视觉数据集，支持果实检测与状态感知研究。
Still image and spatial-temporal tomato data enabling detection, segmentation, tracking, and video-instance segmentation using strong and weak labels
- 构建静止图像与视频数据集，含像素级成熟度标注。
- 视频数据集提供个体果实计数与成熟度信息，支持时序分析。
- 适用于农业视觉感知、植物表型分析等领域的算法验证。
本文发布两个用于商业化种植环境下番茄植株视觉感知的数据集。BUTom21包含由机器人采集的静止图像及人工标注；BUTom-ST21则为基于视频的数据集，采用AI生成伪标签进行半自动标注。两个数据集均提供果实成熟度的像素级标签。目标是为研究社区提供具有挑战性的真实世界影像数据，推动田间植物表型分析方法的发展。其中，时空数据集还包含个体果实数量与成熟度信息，有助于深入探索作物状态感知技术。
原文摘要 · Abstract (English)
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.
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