mlsanityData to 7 Oct 2026

Still image and spatial-temporal tomato data enabling detection, segmentation, tracking, and video-instance segmentation using strong and weak labels

arXiv 2607.14934 ↗First posted 16 Jul 2026cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 32 of its 52 references that are in the map. First posted on arXiv 16 Jul 2026.

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32 of 52 references.

References · 32

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